Seasonal 51-AutoTS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from autots import AutoTS\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = pd.to_datetime('2023-05-19')\n",
    "\n",
    "long = True\n",
    "\n",
    "# Read data from Excel file\n",
    "df_all = pd.read_excel(r\"C:\\Users\\path to temp_data.xlsx\")\n",
    "\n",
    "df_all['Date'] = pd.to_datetime(df_all['Date'], format=\"%d-%m-%Y\")\n",
    "\n",
    "# add last 15 days of data as original values\n",
    "df_all['Original_Value'] = df_all.apply(lambda x: x['Value'] if x['Date'] >= dt else None, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoTS(\n",
    "    forecast_length=15,\n",
    "    frequency='infer',\n",
    "    prediction_interval=0.9,\n",
    "    ensemble='auto',\n",
    "    model_list=\"fast_parallel\",  \n",
    "    transformer_list=\"fast\",\n",
    "    drop_most_recent=1,\n",
    "    max_generations=5,\n",
    "    num_validations=4,\n",
    "    validation_method=\"seasonal 51\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data frequency is: W-FRI, used frequency is: W-FRI\n",
      "Model Number: 1 with model ARIMA in generation 0 of 5\n",
      "Model Number: 2 with model AverageValueNaive in generation 0 of 5\n",
      "Model Number: 3 with model AverageValueNaive in generation 0 of 5\n",
      "Model Number: 4 with model AverageValueNaive in generation 0 of 5\n",
      "Model Number: 5 with model DatepartRegression in generation 0 of 5\n",
      "Model Number: 6 with model DatepartRegression in generation 0 of 5\n",
      "Model Number: 7 with model DatepartRegression in generation 0 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:546: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 8 with model DatepartRegression in generation 0 of 5\n",
      "Template Eval Error: ImportError('Tensorflow not available, install with pip install tensorflow.') in model 8 in generation 0: DatepartRegression\n",
      "Model Number: 9 with model ETS in generation 0 of 5\n",
      "Model Number: 10 with model ETS in generation 0 of 5\n",
      "Model Number: 11 with model GLM in generation 0 of 5\n",
      "Model Number: 12 with model GLM in generation 0 of 5\n",
      "Model Number: 13 with model GLS in generation 0 of 5\n",
      "Model Number: 14 with model GLS in generation 0 of 5\n",
      "Model Number: 15 with model LastValueNaive in generation 0 of 5\n",
      "Model Number: 16 with model LastValueNaive in generation 0 of 5\n",
      "Model Number: 17 with model LastValueNaive in generation 0 of 5\n",
      "Model Number: 18 with model LastValueNaive in generation 0 of 5\n",
      "Model Number: 19 with model SeasonalNaive in generation 0 of 5\n",
      "Model Number: 20 with model SeasonalNaive in generation 0 of 5\n",
      "Model Number: 21 with model SeasonalNaive in generation 0 of 5\n",
      "Model Number: 22 with model UnobservedComponents in generation 0 of 5\n",
      "Model Number: 23 with model UnobservedComponents in generation 0 of 5\n",
      "Model Number: 24 with model UnobservedComponents in generation 0 of 5\n",
      "Model Number: 25 with model VAR in generation 0 of 5\n",
      "Model Number: 26 with model VAR in generation 0 of 5\n",
      "Model Number: 27 with model VECM in generation 0 of 5\n",
      "Model Number: 28 with model VECM in generation 0 of 5\n",
      "Model Number: 29 with model WindowRegression in generation 0 of 5\n",
      "Model Number: 30 with model ConstantNaive in generation 0 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:546: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 31 with model FBProphet in generation 0 of 5\n",
      "Model Number: 32 with model DatepartRegression in generation 0 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 32 in generation 0: DatepartRegression\n",
      "Model Number: 33 with model SeasonalNaive in generation 0 of 5\n",
      "Model Number: 34 with model DatepartRegression in generation 0 of 5\n",
      "Model Number: 35 with model UnobservedComponents in generation 0 of 5\n",
      "Model Number: 36 with model UnobservedComponents in generation 0 of 5\n",
      "Model Number: 37 with model ETS in generation 0 of 5\n",
      "Model Number: 38 with model VECM in generation 0 of 5\n",
      "Model Number: 39 with model ARDL in generation 0 of 5\n",
      "Model Number: 40 with model MultivariateMotif in generation 0 of 5\n",
      "Model Number: 41 with model MultivariateMotif in generation 0 of 5\n",
      "Model Number: 42 with model UnivariateMotif in generation 0 of 5\n",
      "Model Number: 43 with model UnivariateMotif in generation 0 of 5\n",
      "Model Number: 44 with model SectionalMotif in generation 0 of 5\n",
      "Model Number: 45 with model SectionalMotif in generation 0 of 5\n",
      "Model Number: 46 with model FBProphet in generation 0 of 5\n",
      "Model Number: 47 with model SeasonalNaive in generation 0 of 5\n",
      "Model Number: 48 with model DatepartRegression in generation 0 of 5\n",
      "Model Number: 49 with model NVAR in generation 0 of 5\n",
      "Model Number: 50 with model Theta in generation 0 of 5\n",
      "Model Number: 51 with model ARCH in generation 0 of 5\n",
      "Model Number: 52 with model Cassandra in generation 0 of 5\n",
      "Model Number: 53 with model SeasonalityMotif in generation 0 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 54 with model ETS in generation 0 of 5\n",
      "Model Number: 55 with model FBProphet in generation 0 of 5\n",
      "Model Number: 56 with model ARIMA in generation 0 of 5\n",
      "Model Number: 57 with model GLM in generation 0 of 5\n",
      "Model Number: 58 with model UnobservedComponents in generation 0 of 5\n",
      "Template Eval Error: ValueError('Model UnobservedComponents returned NaN for one or more series. fail_on_forecast_nan=True') in model 58 in generation 0: UnobservedComponents\n",
      "Model Number: 59 with model UnivariateMotif in generation 0 of 5\n",
      "Model Number: 60 with model MultivariateMotif in generation 0 of 5\n",
      "Model Number: 61 with model Theta in generation 0 of 5\n",
      "Model Number: 62 with model ARDL in generation 0 of 5\n",
      "Model Number: 63 with model ARCH in generation 0 of 5\n",
      "Model Number: 64 with model ConstantNaive in generation 0 of 5\n",
      "Model Number: 65 with model LastValueNaive in generation 0 of 5\n",
      "Model Number: 66 with model AverageValueNaive in generation 0 of 5\n",
      "Model Number: 67 with model GLS in generation 0 of 5\n",
      "Model Number: 68 with model SeasonalNaive in generation 0 of 5\n",
      "Model Number: 69 with model VAR in generation 0 of 5\n",
      "Model Number: 70 with model VECM in generation 0 of 5\n",
      "Model Number: 71 with model WindowRegression in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer AnomalyRemoval failed on fit') in model 71 in generation 0: WindowRegression\n",
      "Model Number: 72 with model DatepartRegression in generation 0 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 72 in generation 0: DatepartRegression\n",
      "Model Number: 73 with model SectionalMotif in generation 0 of 5\n",
      "Model Number: 74 with model NVAR in generation 0 of 5\n",
      "Model Number: 75 with model MAR in generation 0 of 5\n",
      "Model Number: 76 with model RRVAR in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 76 in generation 0: RRVAR\n",
      "Model Number: 77 with model KalmanStateSpace in generation 0 of 5\n",
      "Model Number: 78 with model MetricMotif in generation 0 of 5\n",
      "Model Number: 79 with model Cassandra in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 79 in generation 0: Cassandra\n",
      "Model Number: 80 with model SeasonalityMotif in generation 0 of 5\n",
      "Model Number: 81 with model FFT in generation 0 of 5\n",
      "Model Number: 82 with model BallTreeMultivariateMotif in generation 0 of 5\n",
      "Template Eval Error: TypeError('1D weights expected when shapes of a and weights differ.') in model 82 in generation 0: BallTreeMultivariateMotif\n",
      "Model Number: 83 with model ConstantNaive in generation 0 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:2412: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 270030507.1339956, tolerance: 104644.38651695284\n",
      "  ) = cd_fast.enet_coordinate_descent_multi_task(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 84 with model MetricMotif in generation 0 of 5\n",
      "Model Number: 85 with model FFT in generation 0 of 5\n",
      "Model Number: 86 with model RRVAR in generation 0 of 5\n",
      "Model Number: 87 with model FFT in generation 0 of 5\n",
      "Model Number: 88 with model MAR in generation 0 of 5\n",
      "Model Number: 89 with model SeasonalNaive in generation 0 of 5\n",
      "Model Number: 90 with model SectionalMotif in generation 0 of 5\n",
      "Model Number: 91 with model UnobservedComponents in generation 0 of 5\n",
      "Model Number: 92 with model Theta in generation 0 of 5\n",
      "Model Number: 93 with model SeasonalNaive in generation 0 of 5\n",
      "Model Number: 94 with model GLS in generation 0 of 5\n",
      "Model Number: 95 with model GLM in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer StandardScaler failed on fit') in model 95 in generation 0: GLM\n",
      "Model Number: 96 with model FBProphet in generation 0 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 96 in generation 0: FBProphet\n",
      "Model Number: 97 with model GLS in generation 0 of 5\n",
      "Model Number: 98 with model LastValueNaive in generation 0 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_ridge.py:200: LinAlgWarning: Ill-conditioned matrix (rcond=4.37717e-25): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 99 with model FBProphet in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 99 in generation 0: FBProphet\n",
      "Model Number: 100 with model UnobservedComponents in generation 0 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:2412: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 338996742.10706997, tolerance: 106741.65145094969\n",
      "  ) = cd_fast.enet_coordinate_descent_multi_task(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 101 with model WindowRegression in generation 0 of 5\n",
      "Model Number: 102 with model Theta in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 102 in generation 0: Theta\n",
      "Model Number: 103 with model GLM in generation 0 of 5\n",
      "Template Eval Error: ValueError('NaN, inf or invalid value detected in weights, estimation infeasible.') in model 103 in generation 0: GLM\n",
      "Model Number: 104 with model VECM in generation 0 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor supplied\") in model 104 in generation 0: VECM\n",
      "Model Number: 105 with model FFT in generation 0 of 5\n",
      "Model Number: 106 with model LastValueNaive in generation 0 of 5\n",
      "Model Number: 107 with model VAR in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer HolidayTransformer failed on fit') in model 107 in generation 0: VAR\n",
      "Model Number: 108 with model LastValueNaive in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 108 in generation 0: LastValueNaive\n",
      "Model Number: 109 with model ARDL in generation 0 of 5\n",
      "Template Eval Error: ValueError(\"ARDL series A failed with error ValueError('The number of regressors (1105) including deterministics, lags of the endog, lags of the exogenous, and fixed regressors is larger than the sample available for estimation (261).') exog train              dp0  dp1        dp2  dp3  dp4  ...  dp270  dp271  dp272  dp273  dp274\\nDate                                        ...                                   \\n2018-01-05   5.0  0.0  2458123.5  1.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2018-01-12  12.0  0.0  2458130.5  1.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2018-01-19  19.0  0.0  2458137.5  1.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2018-01-26  26.0  0.0  2458144.5  1.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2018-02-02   2.0  0.0  2458151.5  0.0  1.0  ...    0.0    0.0    0.0    0.0    0.0\\n...          ...  ...        ...  ...  ...  ...    ...    ...    ...    ...    ...\\n2022-12-23  23.0  0.0  2459936.5  0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2022-12-30  30.0  0.0  2459943.5  0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-01-06   6.0  0.0  2459950.5  1.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-01-13  13.0  0.0  2459957.5  1.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-01-20  20.0  0.0  2459964.5  1.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n\\n[264 rows x 275 columns] and predict              dp0  dp1        dp2  dp3  dp4  ...  dp270  dp271  dp272  dp273  dp274\\n2023-01-27  27.0  0.0  2459971.5  1.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-02-03   3.0  0.0  2459978.5  0.0  1.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-02-10  10.0  0.0  2459985.5  0.0  1.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-02-17  17.0  0.0  2459992.5  0.0  1.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-02-24  24.0  0.0  2459999.5  0.0  1.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-03-03   3.0  0.0  2460006.5  0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-03-10  10.0  0.0  2460013.5  0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-03-17  17.0  0.0  2460020.5  0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-03-24  24.0  0.0  2460027.5  0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-03-31  31.0  0.0  2460034.5  0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-04-07   7.0  0.0  2460041.5  0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-04-14  14.0  0.0  2460048.5  0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-04-21  21.0  0.0  2460055.5  0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-04-28  28.0  0.0  2460062.5  0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n2023-05-05   5.0  0.0  2460069.5  0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0\\n\\n[15 rows x 275 columns]\") in model 109 in generation 0: ARDL\n",
      "Model Number: 110 with model ETS in generation 0 of 5\n",
      "Model Number: 111 with model FFT in generation 0 of 5\n",
      "Model Number: 112 with model RRVAR in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 112 in generation 0: RRVAR\n",
      "Model Number: 113 with model DatepartRegression in generation 0 of 5\n",
      "Template Eval Error: ImportError('Tensorflow not available, install with pip install tensorflow.') in model 113 in generation 0: DatepartRegression\n",
      "Model Number: 114 with model Theta in generation 0 of 5\n",
      "Model Number: 115 with model LastValueNaive in generation 0 of 5\n",
      "Model Number: 116 with model FBProphet in generation 0 of 5\n",
      "Model Number: 117 with model LastValueNaive in generation 0 of 5\n",
      "Model Number: 118 with model KalmanStateSpace in generation 0 of 5\n",
      "Model Number: 119 with model Theta in generation 0 of 5\n",
      "Model Number: 120 with model SeasonalityMotif in generation 0 of 5\n",
      "Model Number: 121 with model GLM in generation 0 of 5\n",
      "Template Eval Error: ValueError('The first guess on the deviance function returned a nan.  This could be a boundary  problem and should be reported.') in model 121 in generation 0: GLM\n",
      "Model Number: 122 with model SeasonalNaive in generation 0 of 5\n",
      "Model Number: 123 with model AverageValueNaive in generation 0 of 5\n",
      "Model Number: 124 with model LastValueNaive in generation 0 of 5\n",
      "Model Number: 125 with model SeasonalityMotif in generation 0 of 5\n",
      "Model Number: 126 with model WindowRegression in generation 0 of 5\n",
      "Model Number: 127 with model VECM in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 127 in generation 0: VECM\n",
      "Model Number: 128 with model SeasonalNaive in generation 0 of 5\n",
      "Model Number: 129 with model SeasonalNaive in generation 0 of 5\n",
      "Model Number: 130 with model ETS in generation 0 of 5\n",
      "Model Number: 131 with model ETS in generation 0 of 5\n",
      "Model Number: 132 with model ETS in generation 0 of 5\n",
      "Model Number: 133 with model SectionalMotif in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 133 in generation 0: SectionalMotif\n",
      "Model Number: 134 with model MetricMotif in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 134 in generation 0: MetricMotif\n",
      "Model Number: 135 with model GLM in generation 0 of 5\n",
      "Template Eval Error: ValueError('NaN, inf or invalid value detected in weights, estimation infeasible.') in model 135 in generation 0: GLM\n",
      "Model Number: 136 with model GLM in generation 0 of 5\n",
      "Template Eval Error: ValueError('The first guess on the deviance function returned a nan.  This could be a boundary  problem and should be reported.') in model 136 in generation 0: GLM\n",
      "Model Number: 137 with model KalmanStateSpace in generation 0 of 5\n",
      "Model Number: 138 with model RRVAR in generation 0 of 5\n",
      "Model Number: 139 with model UnobservedComponents in generation 0 of 5\n",
      "Model Number: 140 with model ARIMA in generation 0 of 5\n",
      "Model Number: 141 with model BallTreeMultivariateMotif in generation 0 of 5\n",
      "Model Number: 142 with model LastValueNaive in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 142 in generation 0: LastValueNaive\n",
      "Model Number: 143 with model LastValueNaive in generation 0 of 5\n",
      "Model Number: 144 with model GLS in generation 0 of 5\n",
      "Model Number: 145 with model BallTreeMultivariateMotif in generation 0 of 5\n",
      "Model Number: 146 with model ConstantNaive in generation 0 of 5\n",
      "Model Number: 147 with model ETS in generation 0 of 5\n",
      "Model Number: 148 with model FBProphet in generation 0 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_ridge.py:200: LinAlgWarning: Ill-conditioned matrix (rcond=4.37717e-25): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 149 with model RRVAR in generation 0 of 5\n",
      "Model Number: 150 with model SeasonalNaive in generation 0 of 5\n",
      "Model Number: 151 with model SeasonalNaive in generation 0 of 5\n",
      "Model Number: 152 with model AverageValueNaive in generation 0 of 5\n",
      "Model Number: 153 with model VAR in generation 0 of 5\n",
      "Model Number: 154 with model RRVAR in generation 0 of 5\n",
      "Model Number: 155 with model RRVAR in generation 0 of 5\n",
      "Model Number: 156 with model SeasonalityMotif in generation 0 of 5\n",
      "Model Number: 157 with model GLS in generation 0 of 5\n",
      "Model Number: 158 with model RRVAR in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer Detrend failed on fit') in model 158 in generation 0: RRVAR\n",
      "Model Number: 159 with model WindowRegression in generation 0 of 5\n",
      "Model Number: 160 with model GLS in generation 0 of 5\n",
      "Model Number: 161 with model ConstantNaive in generation 0 of 5\n",
      "Model Number: 162 with model KalmanStateSpace in generation 0 of 5\n",
      "Model Number: 163 with model MultivariateMotif in generation 0 of 5\n",
      "Model Number: 164 with model LastValueNaive in generation 0 of 5\n",
      "Model Number: 165 with model Cassandra in generation 0 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 166 with model SeasonalNaive in generation 0 of 5\n",
      "Model Number: 167 with model MAR in generation 0 of 5\n",
      "Template Eval Error: ValueError('Shape of passed values is (7, 5), indices imply (15, 5)') in model 167 in generation 0: MAR\n",
      "Model Number: 168 with model MetricMotif in generation 0 of 5\n",
      "Model Number: 169 with model VAR in generation 0 of 5\n",
      "Template Eval Error: IndexError('index 0 is out of bounds for axis 0 with size 0') in model 169 in generation 0: VAR\n",
      "Model Number: 170 with model MultivariateMotif in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer Detrend failed on fit') in model 170 in generation 0: MultivariateMotif\n",
      "Model Number: 171 with model KalmanStateSpace in generation 0 of 5\n",
      "Model Number: 172 with model AverageValueNaive in generation 0 of 5\n",
      "Model Number: 173 with model LastValueNaive in generation 0 of 5\n",
      "Model Number: 174 with model ETS in generation 0 of 5\n",
      "Model Number: 175 with model Theta in generation 0 of 5\n",
      "Model Number: 176 with model ARCH in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 176 in generation 0: ARCH\n",
      "Model Number: 177 with model VAR in generation 0 of 5\n",
      "Model Number: 178 with model MAR in generation 0 of 5\n",
      "Model Number: 179 with model BallTreeMultivariateMotif in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 179 in generation 0: BallTreeMultivariateMotif\n",
      "Model Number: 180 with model KalmanStateSpace in generation 0 of 5\n",
      "Model Number: 181 with model MAR in generation 0 of 5\n",
      "Model Number: 182 with model BallTreeMultivariateMotif in generation 0 of 5\n",
      "Model Number: 183 with model LastValueNaive in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 183 in generation 0: LastValueNaive\n",
      "Model Number: 184 with model BallTreeMultivariateMotif in generation 0 of 5\n",
      "Template Eval Error: TypeError('1D weights expected when shapes of a and weights differ.') in model 184 in generation 0: BallTreeMultivariateMotif\n",
      "Model Number: 185 with model MetricMotif in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer RegressionFilter failed on fit') in model 185 in generation 0: MetricMotif\n",
      "Model Number: 186 with model MultivariateMotif in generation 0 of 5\n",
      "Model Number: 187 with model GLM in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 187 in generation 0: GLM\n",
      "Model Number: 188 with model UnivariateMotif in generation 0 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 188 in generation 0: UnivariateMotif\n",
      "Model Number: 189 with model FFT in generation 0 of 5\n",
      "Model Number: 190 with model VAR in generation 0 of 5\n",
      "Model Number: 191 with model KalmanStateSpace in generation 0 of 5\n",
      "Model Number: 192 with model ConstantNaive in generation 0 of 5\n",
      "Model Number: 193 with model GLS in generation 0 of 5\n",
      "Model Number: 194 with model GLS in generation 0 of 5\n",
      "Model Number: 195 with model SeasonalityMotif in generation 0 of 5\n",
      "Model Number: 196 with model GLM in generation 0 of 5\n",
      "Model Number: 197 with model DatepartRegression in generation 0 of 5\n",
      "Model Number: 198 with model LastValueNaive in generation 0 of 5\n",
      "New Generation: 1 of 5\n",
      "Model Number: 199 with model GLS in generation 1 of 5\n",
      "Model Number: 200 with model ConstantNaive in generation 1 of 5\n",
      "Model Number: 201 with model MultivariateMotif in generation 1 of 5\n",
      "Model Number: 202 with model VECM in generation 1 of 5\n",
      "Model Number: 203 with model LastValueNaive in generation 1 of 5\n",
      "Model Number: 204 with model ARCH in generation 1 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but future_regressor not supplied\") in model 204 in generation 1: ARCH\n",
      "Model Number: 205 with model FBProphet in generation 1 of 5\n",
      "Model Number: 206 with model GLS in generation 1 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_ridge.py:200: LinAlgWarning: Ill-conditioned matrix (rcond=4.37717e-25): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 207 with model FBProphet in generation 1 of 5\n",
      "Model Number: 208 with model MAR in generation 1 of 5\n",
      "Template Eval Error: ValueError('Shape of passed values is (14, 5), indices imply (15, 5)') in model 208 in generation 1: MAR\n",
      "Model Number: 209 with model MAR in generation 1 of 5\n",
      "Model Number: 210 with model VAR in generation 1 of 5\n",
      "Model Number: 211 with model RRVAR in generation 1 of 5\n",
      "Model Number: 212 with model GLS in generation 1 of 5\n",
      "Model Number: 213 with model VECM in generation 1 of 5\n",
      "Model Number: 214 with model Theta in generation 1 of 5\n",
      "Model Number: 215 with model GLS in generation 1 of 5\n",
      "Model Number: 216 with model ETS in generation 1 of 5\n",
      "Model Number: 217 with model SeasonalityMotif in generation 1 of 5\n",
      "Model Number: 218 with model VAR in generation 1 of 5\n",
      "Model Number: 219 with model ETS in generation 1 of 5\n",
      "Model Number: 220 with model Cassandra in generation 1 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 221 with model SeasonalNaive in generation 1 of 5\n",
      "Model Number: 222 with model VAR in generation 1 of 5\n",
      "Model Number: 223 with model SeasonalNaive in generation 1 of 5\n",
      "Model Number: 224 with model MAR in generation 1 of 5\n",
      "Model Number: 225 with model SeasonalityMotif in generation 1 of 5\n",
      "Model Number: 226 with model FBProphet in generation 1 of 5\n",
      "Model Number: 227 with model Cassandra in generation 1 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 228 with model SeasonalityMotif in generation 1 of 5\n",
      "Model Number: 229 with model Theta in generation 1 of 5\n",
      "Model Number: 230 with model ConstantNaive in generation 1 of 5\n",
      "Model Number: 231 with model ETS in generation 1 of 5\n",
      "Model Number: 232 with model WindowRegression in generation 1 of 5\n",
      "Template Eval Error: XGBoostError('[22:11:24] C:\\\\buildkite-agent\\\\builds\\\\buildkite-windows-cpu-autoscaling-group-i-0750514818a16474a-1\\\\xgboost\\\\xgboost-ci-windows\\\\src\\\\objective\\\\quantile_obj.cu:40: Check failed: info.labels.Shape(1) == 1 (15 vs. 1) : Multi-target is not yet supported by the quantile loss.') in model 232 in generation 1: WindowRegression\n",
      "Model Number: 233 with model FFT in generation 1 of 5\n",
      "Model Number: 234 with model VAR in generation 1 of 5\n",
      "Template Eval Error: LinAlgError('5-th leading minor of the array is not positive definite') in model 234 in generation 1: VAR\n",
      "Model Number: 235 with model MultivariateMotif in generation 1 of 5\n",
      "Model Number: 236 with model SeasonalityMotif in generation 1 of 5\n",
      "Model Number: 237 with model Theta in generation 1 of 5\n",
      "Model Number: 238 with model VECM in generation 1 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor supplied\") in model 238 in generation 1: VECM\n",
      "Model Number: 239 with model MetricMotif in generation 1 of 5\n",
      "Model Number: 240 with model ConstantNaive in generation 1 of 5\n",
      "Model Number: 241 with model LastValueNaive in generation 1 of 5\n",
      "Model Number: 242 with model MultivariateMotif in generation 1 of 5\n",
      "Model Number: 243 with model UnobservedComponents in generation 1 of 5\n",
      "Model Number: 244 with model WindowRegression in generation 1 of 5\n",
      "Template Eval Error: XGBoostError('[22:16:42] C:\\\\buildkite-agent\\\\builds\\\\buildkite-windows-cpu-autoscaling-group-i-0750514818a16474a-1\\\\xgboost\\\\xgboost-ci-windows\\\\src\\\\objective\\\\regression_obj.cu:530: GammaRegression: label must be positive.') in model 244 in generation 1: WindowRegression\n",
      "Model Number: 245 with model ARCH in generation 1 of 5\n",
      "Model Number: 246 with model VAR in generation 1 of 5\n",
      "Model Number: 247 with model VECM in generation 1 of 5\n",
      "Model Number: 248 with model AverageValueNaive in generation 1 of 5\n",
      "Model Number: 249 with model MAR in generation 1 of 5\n",
      "Model Number: 250 with model MultivariateMotif in generation 1 of 5\n",
      "Model Number: 251 with model Theta in generation 1 of 5\n",
      "Model Number: 252 with model KalmanStateSpace in generation 1 of 5\n",
      "Model Number: 253 with model DatepartRegression in generation 1 of 5\n",
      "Model Number: 254 with model ConstantNaive in generation 1 of 5\n",
      "Model Number: 255 with model FFT in generation 1 of 5\n",
      "Model Number: 256 with model Theta in generation 1 of 5\n",
      "Model Number: 257 with model BallTreeMultivariateMotif in generation 1 of 5\n",
      "Model Number: 258 with model AverageValueNaive in generation 1 of 5\n",
      "Model Number: 259 with model MAR in generation 1 of 5\n",
      "Model Number: 260 with model DatepartRegression in generation 1 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 260 in generation 1: DatepartRegression\n",
      "Model Number: 261 with model FBProphet in generation 1 of 5\n",
      "Model Number: 262 with model FBProphet in generation 1 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\tools\\thresholding.py:204: RuntimeWarning: overflow encountered in double_scalars\n",
      "  (1 + mean_perc_decrease) ** self.mean_weight\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\tools\\thresholding.py:204: RuntimeWarning: overflow encountered in double_scalars\n",
      "  (1 + mean_perc_decrease) ** self.mean_weight\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\tools\\thresholding.py:204: RuntimeWarning: overflow encountered in double_scalars\n",
      "  (1 + mean_perc_decrease) ** self.mean_weight\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 263 with model SectionalMotif in generation 1 of 5\n",
      "Model Number: 264 with model KalmanStateSpace in generation 1 of 5\n",
      "Model Number: 265 with model RRVAR in generation 1 of 5\n",
      "Model Number: 266 with model UnivariateMotif in generation 1 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 266 in generation 1: UnivariateMotif\n",
      "Model Number: 267 with model UnobservedComponents in generation 1 of 5\n",
      "Model Number: 268 with model DatepartRegression in generation 1 of 5\n",
      "Model Number: 269 with model VAR in generation 1 of 5\n",
      "Model Number: 270 with model ETS in generation 1 of 5\n",
      "Model Number: 271 with model SeasonalNaive in generation 1 of 5\n",
      "Model Number: 272 with model RRVAR in generation 1 of 5\n",
      "Model Number: 273 with model ETS in generation 1 of 5\n",
      "Model Number: 274 with model ARCH in generation 1 of 5\n",
      "Template Eval Error: ValueError(\"Model returned NaN due to a preprocessing transformer {'fillna': 'ffill', 'transformations': {'0': 'RobustScaler', '1': 'PowerTransformer'}, 'transformation_params': {'0': {}, '1': {}}}. fail_on_forecast_nan=True\") in model 274 in generation 1: ARCH\n",
      "Model Number: 275 with model SeasonalNaive in generation 1 of 5\n",
      "Model Number: 276 with model SeasonalNaive in generation 1 of 5\n",
      "Model Number: 277 with model WindowRegression in generation 1 of 5\n",
      "Model Number: 278 with model VECM in generation 1 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor supplied\") in model 278 in generation 1: VECM\n",
      "Model Number: 279 with model LastValueNaive in generation 1 of 5\n",
      "Model Number: 280 with model AverageValueNaive in generation 1 of 5\n",
      "Model Number: 281 with model MAR in generation 1 of 5\n",
      "Model Number: 282 with model WindowRegression in generation 1 of 5\n",
      "Model Number: 283 with model SeasonalNaive in generation 1 of 5\n",
      "Model Number: 284 with model AverageValueNaive in generation 1 of 5\n",
      "Model Number: 285 with model ARIMA in generation 1 of 5\n",
      "Model Number: 286 with model ConstantNaive in generation 1 of 5\n",
      "Model Number: 287 with model SeasonalNaive in generation 1 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:2412: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 270030507.1339956, tolerance: 104644.38651695284\n",
      "  ) = cd_fast.enet_coordinate_descent_multi_task(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 288 with model RRVAR in generation 1 of 5\n",
      "Model Number: 289 with model ARDL in generation 1 of 5\n",
      "Model Number: 290 with model MAR in generation 1 of 5\n",
      "Model Number: 291 with model SeasonalityMotif in generation 1 of 5\n",
      "Model Number: 292 with model SeasonalNaive in generation 1 of 5\n",
      "Model Number: 293 with model Theta in generation 1 of 5\n",
      "Model Number: 294 with model VAR in generation 1 of 5\n",
      "Model Number: 295 with model DatepartRegression in generation 1 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 295 in generation 1: DatepartRegression\n",
      "Model Number: 296 with model UnivariateMotif in generation 1 of 5\n",
      "Model Number: 297 with model FFT in generation 1 of 5\n",
      "Model Number: 298 with model SectionalMotif in generation 1 of 5\n",
      "Model Number: 299 with model FBProphet in generation 1 of 5\n",
      "Model Number: 300 with model FFT in generation 1 of 5\n",
      "Model Number: 301 with model SectionalMotif in generation 1 of 5\n",
      "Model Number: 302 with model ETS in generation 1 of 5\n",
      "Model Number: 303 with model Theta in generation 1 of 5\n",
      "Model Number: 304 with model ARIMA in generation 1 of 5\n",
      "Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 304 in generation 1: ARIMA\n",
      "Model Number: 305 with model MultivariateMotif in generation 1 of 5\n",
      "Model Number: 306 with model GLS in generation 1 of 5\n",
      "Model Number: 307 with model RRVAR in generation 1 of 5\n",
      "Model Number: 308 with model ETS in generation 1 of 5\n",
      "Model Number: 309 with model KalmanStateSpace in generation 1 of 5\n",
      "Model Number: 310 with model KalmanStateSpace in generation 1 of 5\n",
      "Model Number: 311 with model MAR in generation 1 of 5\n",
      "Template Eval Error: ValueError('Shape of passed values is (7, 5), indices imply (15, 5)') in model 311 in generation 1: MAR\n",
      "Model Number: 312 with model SeasonalityMotif in generation 1 of 5\n",
      "Model Number: 313 with model WindowRegression in generation 1 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:546: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 314 with model WindowRegression in generation 1 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:546: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 315 with model ETS in generation 1 of 5\n",
      "Template Eval Error: ValueError('Model ETS returned NaN for one or more series. fail_on_forecast_nan=True') in model 315 in generation 1: ETS\n",
      "Model Number: 316 with model LastValueNaive in generation 1 of 5\n",
      "Model Number: 317 with model LastValueNaive in generation 1 of 5\n",
      "Model Number: 318 with model ConstantNaive in generation 1 of 5\n",
      "Model Number: 319 with model AverageValueNaive in generation 1 of 5\n",
      "Model Number: 320 with model SeasonalNaive in generation 1 of 5\n",
      "Model Number: 321 with model FBProphet in generation 1 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_ridge.py:200: LinAlgWarning: Ill-conditioned matrix (rcond=4.37717e-25): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 322 with model ETS in generation 1 of 5\n",
      "Model Number: 323 with model GLM in generation 1 of 5\n",
      "Template Eval Error: ValueError('The first guess on the deviance function returned a nan.  This could be a boundary  problem and should be reported.') in model 323 in generation 1: GLM\n",
      "Model Number: 324 with model FFT in generation 1 of 5\n",
      "Model Number: 325 with model FBProphet in generation 1 of 5\n",
      "Model Number: 326 with model ETS in generation 1 of 5\n",
      "Model Number: 327 with model ARCH in generation 1 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but future_regressor not supplied\") in model 327 in generation 1: ARCH\n",
      "Model Number: 328 with model LastValueNaive in generation 1 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 328 in generation 1: LastValueNaive\n",
      "Model Number: 329 with model SeasonalNaive in generation 1 of 5\n",
      "Model Number: 330 with model ETS in generation 1 of 5\n",
      "Model Number: 331 with model KalmanStateSpace in generation 1 of 5\n",
      "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 331 in generation 1: KalmanStateSpace\n",
      "Model Number: 332 with model ETS in generation 1 of 5\n",
      "Model Number: 333 with model KalmanStateSpace in generation 1 of 5\n",
      "Model Number: 334 with model Theta in generation 1 of 5\n",
      "Model Number: 335 with model MultivariateMotif in generation 1 of 5\n",
      "Model Number: 336 with model VAR in generation 1 of 5\n",
      "Template Eval Error: IndexError('index 0 is out of bounds for axis 0 with size 0') in model 336 in generation 1: VAR\n",
      "Model Number: 337 with model LastValueNaive in generation 1 of 5\n",
      "Model Number: 338 with model VECM in generation 1 of 5\n",
      "Model Number: 339 with model SectionalMotif in generation 1 of 5\n",
      "Model Number: 340 with model NVAR in generation 1 of 5\n",
      "Model Number: 341 with model ARDL in generation 1 of 5\n",
      "Template Eval Error: ValueError(\"ARDL series B failed with error IndexError('tuple index out of range') exog train None and predict None\") in model 341 in generation 1: ARDL\n",
      "Model Number: 342 with model ConstantNaive in generation 1 of 5\n",
      "Model Number: 343 with model UnivariateMotif in generation 1 of 5\n",
      "Template Eval Error: Exception('Transformer SinTrend failed on fit') in model 343 in generation 1: UnivariateMotif\n",
      "New Generation: 2 of 5\n",
      "Model Number: 344 with model LastValueNaive in generation 2 of 5\n",
      "Model Number: 345 with model NVAR in generation 2 of 5\n",
      "Template Eval Error: ValueError('Model NVAR returned NaN for one or more series. fail_on_forecast_nan=True') in model 345 in generation 2: NVAR\n",
      "Model Number: 346 with model LastValueNaive in generation 2 of 5\n",
      "Model Number: 347 with model ETS in generation 2 of 5\n",
      "Model Number: 348 with model WindowRegression in generation 2 of 5\n",
      "Model Number: 349 with model SeasonalityMotif in generation 2 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.675e+05, tolerance: 1.037e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 350 with model ARIMA in generation 2 of 5\n",
      "Model Number: 351 with model MultivariateMotif in generation 2 of 5\n",
      "Model Number: 352 with model MetricMotif in generation 2 of 5\n",
      "Model Number: 353 with model ARCH in generation 2 of 5\n",
      "Model Number: 354 with model SeasonalityMotif in generation 2 of 5\n",
      "Model Number: 355 with model Theta in generation 2 of 5\n",
      "Model Number: 356 with model FBProphet in generation 2 of 5\n",
      "Model Number: 357 with model ConstantNaive in generation 2 of 5\n",
      "Model Number: 358 with model GLS in generation 2 of 5\n",
      "Model Number: 359 with model ETS in generation 2 of 5\n",
      "Model Number: 360 with model SeasonalityMotif in generation 2 of 5\n",
      "Model Number: 361 with model WindowRegression in generation 2 of 5\n",
      "Model Number: 362 with model UnobservedComponents in generation 2 of 5\n",
      "Model Number: 363 with model SectionalMotif in generation 2 of 5\n",
      "Model Number: 364 with model Theta in generation 2 of 5\n",
      "Model Number: 365 with model VAR in generation 2 of 5\n",
      "Model Number: 366 with model MAR in generation 2 of 5\n",
      "Model Number: 367 with model SectionalMotif in generation 2 of 5\n",
      "Model Number: 368 with model DatepartRegression in generation 2 of 5\n",
      "Model Number: 369 with model AverageValueNaive in generation 2 of 5\n",
      "Model Number: 370 with model ARDL in generation 2 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but future_regressor not supplied\") in model 370 in generation 2: ARDL\n",
      "Model Number: 371 with model FBProphet in generation 2 of 5\n",
      "Model Number: 372 with model VAR in generation 2 of 5\n",
      "Template Eval Error: ValueError('maxlags is too large for the number of observations and the number of equations. The largest model cannot be estimated.') in model 372 in generation 2: VAR\n",
      "Model Number: 373 with model VAR in generation 2 of 5\n",
      "Model Number: 374 with model BallTreeMultivariateMotif in generation 2 of 5\n",
      "Model Number: 375 with model VAR in generation 2 of 5\n",
      "Model Number: 376 with model VAR in generation 2 of 5\n",
      "Model Number: 377 with model ARDL in generation 2 of 5\n",
      "Template Eval Error: ValueError(\"ARDL series A failed with error IndexError('tuple index out of range') exog train None and predict None\") in model 377 in generation 2: ARDL\n",
      "Model Number: 378 with model ETS in generation 2 of 5\n",
      "Model Number: 379 with model VECM in generation 2 of 5\n",
      "Model Number: 380 with model Theta in generation 2 of 5\n",
      "Model Number: 381 with model LastValueNaive in generation 2 of 5\n",
      "Model Number: 382 with model LastValueNaive in generation 2 of 5\n",
      "Model Number: 383 with model SeasonalityMotif in generation 2 of 5\n",
      "Model Number: 384 with model GLM in generation 2 of 5\n",
      "Model Number: 385 with model UnobservedComponents in generation 2 of 5\n",
      "Model Number: 386 with model VAR in generation 2 of 5\n",
      "Model Number: 387 with model UnobservedComponents in generation 2 of 5\n",
      "Model Number: 388 with model ARIMA in generation 2 of 5\n",
      "Model Number: 389 with model SectionalMotif in generation 2 of 5\n",
      "Model Number: 390 with model MAR in generation 2 of 5\n",
      "Model Number: 391 with model Theta in generation 2 of 5\n",
      "Model Number: 392 with model DatepartRegression in generation 2 of 5\n",
      "Model Number: 393 with model MultivariateMotif in generation 2 of 5\n",
      "Model Number: 394 with model VAR in generation 2 of 5\n",
      "Template Eval Error: IndexError('index 0 is out of bounds for axis 0 with size 0') in model 394 in generation 2: VAR\n",
      "Model Number: 395 with model AverageValueNaive in generation 2 of 5\n",
      "Model Number: 396 with model UnivariateMotif in generation 2 of 5\n",
      "Model Number: 397 with model Theta in generation 2 of 5\n",
      "Template Eval Error: Exception('Transformer DatepartRegression failed on inverse') in model 397 in generation 2: Theta\n",
      "Model Number: 398 with model UnivariateMotif in generation 2 of 5\n",
      "Model Number: 399 with model VAR in generation 2 of 5\n",
      "Model Number: 400 with model VECM in generation 2 of 5\n",
      "Model Number: 401 with model SeasonalityMotif in generation 2 of 5\n",
      "Model Number: 402 with model VAR in generation 2 of 5\n",
      "Model Number: 403 with model DatepartRegression in generation 2 of 5\n",
      "Model Number: 404 with model AverageValueNaive in generation 2 of 5\n",
      "Model Number: 405 with model VAR in generation 2 of 5\n",
      "Model Number: 406 with model SeasonalityMotif in generation 2 of 5\n",
      "Model Number: 407 with model KalmanStateSpace in generation 2 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 407 in generation 2: KalmanStateSpace\n",
      "Model Number: 408 with model UnobservedComponents in generation 2 of 5\n",
      "Model Number: 409 with model SeasonalNaive in generation 2 of 5\n",
      "Model Number: 410 with model SeasonalNaive in generation 2 of 5\n",
      "Model Number: 411 with model SectionalMotif in generation 2 of 5\n",
      "Model Number: 412 with model FFT in generation 2 of 5\n",
      "Model Number: 413 with model AverageValueNaive in generation 2 of 5\n",
      "Model Number: 414 with model VECM in generation 2 of 5\n",
      "Model Number: 415 with model ConstantNaive in generation 2 of 5\n",
      "Model Number: 416 with model ETS in generation 2 of 5\n",
      "Model Number: 417 with model DatepartRegression in generation 2 of 5\n",
      "Model Number: 418 with model UnobservedComponents in generation 2 of 5\n",
      "Model Number: 419 with model NVAR in generation 2 of 5\n",
      "Model Number: 420 with model MultivariateMotif in generation 2 of 5\n",
      "Model Number: 421 with model SeasonalNaive in generation 2 of 5\n",
      "Model Number: 422 with model ConstantNaive in generation 2 of 5\n",
      "Model Number: 423 with model ETS in generation 2 of 5\n",
      "Model Number: 424 with model Theta in generation 2 of 5\n",
      "Model Number: 425 with model DatepartRegression in generation 2 of 5\n",
      "Model Number: 426 with model Theta in generation 2 of 5\n",
      "Model Number: 427 with model FBProphet in generation 2 of 5\n",
      "Model Number: 428 with model BallTreeMultivariateMotif in generation 2 of 5\n",
      "Template Eval Error: Exception('Transformer Detrend failed on fit') in model 428 in generation 2: BallTreeMultivariateMotif\n",
      "Model Number: 429 with model GLS in generation 2 of 5\n",
      "Template Eval Error: Exception('Transformer DatepartRegression failed on inverse') in model 429 in generation 2: GLS\n",
      "Model Number: 430 with model VECM in generation 2 of 5\n",
      "Model Number: 431 with model ETS in generation 2 of 5\n",
      "Model Number: 432 with model UnobservedComponents in generation 2 of 5\n",
      "Model Number: 433 with model WindowRegression in generation 2 of 5\n",
      "Model Number: 434 with model ConstantNaive in generation 2 of 5\n",
      "Model Number: 435 with model FFT in generation 2 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 435 in generation 2: FFT\n",
      "Model Number: 436 with model RRVAR in generation 2 of 5\n",
      "Model Number: 437 with model SeasonalNaive in generation 2 of 5\n",
      "Model Number: 438 with model WindowRegression in generation 2 of 5\n",
      "Model Number: 439 with model LastValueNaive in generation 2 of 5\n",
      "Model Number: 440 with model SeasonalNaive in generation 2 of 5\n",
      "Model Number: 441 with model SeasonalNaive in generation 2 of 5\n",
      "Model Number: 442 with model SectionalMotif in generation 2 of 5\n",
      "Model Number: 443 with model ETS in generation 2 of 5\n",
      "Model Number: 444 with model LastValueNaive in generation 2 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 444 in generation 2: LastValueNaive\n",
      "Model Number: 445 with model ARDL in generation 2 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but future_regressor not supplied\") in model 445 in generation 2: ARDL\n",
      "Model Number: 446 with model SectionalMotif in generation 2 of 5\n",
      "Model Number: 447 with model VECM in generation 2 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor supplied\") in model 447 in generation 2: VECM\n",
      "Model Number: 448 with model RRVAR in generation 2 of 5\n",
      "Model Number: 449 with model AverageValueNaive in generation 2 of 5\n",
      "Model Number: 450 with model KalmanStateSpace in generation 2 of 5\n",
      "Model Number: 451 with model SectionalMotif in generation 2 of 5\n",
      "Model Number: 452 with model AverageValueNaive in generation 2 of 5\n",
      "Model Number: 453 with model FFT in generation 2 of 5\n",
      "Model Number: 454 with model ARIMA in generation 2 of 5\n",
      "Model Number: 455 with model VAR in generation 2 of 5\n",
      "Model Number: 456 with model VECM in generation 2 of 5\n",
      "Model Number: 457 with model NVAR in generation 2 of 5\n",
      "Model Number: 458 with model Cassandra in generation 2 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 459 with model MultivariateMotif in generation 2 of 5\n",
      "Model Number: 460 with model MultivariateMotif in generation 2 of 5\n",
      "Model Number: 461 with model ETS in generation 2 of 5\n",
      "Model Number: 462 with model FBProphet in generation 2 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 462 in generation 2: FBProphet\n",
      "Model Number: 463 with model Cassandra in generation 2 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 464 with model Cassandra in generation 2 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 465 with model MultivariateMotif in generation 2 of 5\n",
      "Model Number: 466 with model RRVAR in generation 2 of 5\n",
      "Template Eval Error: Exception('Transformer ReplaceConstant failed on fit') in model 466 in generation 2: RRVAR\n",
      "Model Number: 467 with model DatepartRegression in generation 2 of 5\n",
      "Model Number: 468 with model LastValueNaive in generation 2 of 5\n",
      "Model Number: 469 with model AverageValueNaive in generation 2 of 5\n",
      "Model Number: 470 with model VECM in generation 2 of 5\n",
      "Model Number: 471 with model AverageValueNaive in generation 2 of 5\n",
      "Model Number: 472 with model ConstantNaive in generation 2 of 5\n",
      "Model Number: 473 with model GLS in generation 2 of 5\n",
      "Model Number: 474 with model ARIMA in generation 2 of 5\n",
      "Model Number: 475 with model ETS in generation 2 of 5\n",
      "Model Number: 476 with model BallTreeMultivariateMotif in generation 2 of 5\n",
      "Template Eval Error: ValueError('Input contains NaN.') in model 476 in generation 2: BallTreeMultivariateMotif\n",
      "Model Number: 477 with model WindowRegression in generation 2 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 477 in generation 2: WindowRegression\n",
      "Model Number: 478 with model VECM in generation 2 of 5\n",
      "Model Number: 479 with model GLS in generation 2 of 5\n",
      "Model Number: 480 with model Cassandra in generation 2 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 481 with model ETS in generation 2 of 5\n",
      "Model Number: 482 with model MetricMotif in generation 2 of 5\n",
      "Model Number: 483 with model VAR in generation 2 of 5\n",
      "Model Number: 484 with model Theta in generation 2 of 5\n",
      "Model Number: 485 with model FFT in generation 2 of 5\n",
      "Model Number: 486 with model UnobservedComponents in generation 2 of 5\n",
      "Model Number: 487 with model AverageValueNaive in generation 2 of 5\n",
      "Model Number: 488 with model MultivariateMotif in generation 2 of 5\n",
      "New Generation: 3 of 5\n",
      "Model Number: 489 with model Theta in generation 3 of 5\n",
      "Template Eval Error: ValueError('Model Theta returned NaN for one or more series. fail_on_forecast_nan=True') in model 489 in generation 3: Theta\n",
      "Model Number: 490 with model LastValueNaive in generation 3 of 5\n",
      "Model Number: 491 with model AverageValueNaive in generation 3 of 5\n",
      "Model Number: 492 with model WindowRegression in generation 3 of 5\n",
      "Model Number: 493 with model RRVAR in generation 3 of 5\n",
      "Model Number: 494 with model FFT in generation 3 of 5\n",
      "Model Number: 495 with model FFT in generation 3 of 5\n",
      "Model Number: 496 with model UnivariateMotif in generation 3 of 5\n",
      "Model Number: 497 with model ARIMA in generation 3 of 5\n",
      "Model Number: 498 with model UnivariateMotif in generation 3 of 5\n",
      "Model Number: 499 with model DatepartRegression in generation 3 of 5\n",
      "Model Number: 500 with model Theta in generation 3 of 5\n",
      "Model Number: 501 with model LastValueNaive in generation 3 of 5\n",
      "Model Number: 502 with model FFT in generation 3 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:2412: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 297648675.98569155, tolerance: 106777.62472649614\n",
      "  ) = cd_fast.enet_coordinate_descent_multi_task(\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:2412: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 293038085.9410254, tolerance: 105228.17488498626\n",
      "  ) = cd_fast.enet_coordinate_descent_multi_task(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 503 with model AverageValueNaive in generation 3 of 5\n",
      "Model Number: 504 with model ARCH in generation 3 of 5\n",
      "Model Number: 505 with model KalmanStateSpace in generation 3 of 5\n",
      "Model Number: 506 with model AverageValueNaive in generation 3 of 5\n",
      "Model Number: 507 with model AverageValueNaive in generation 3 of 5\n",
      "Model Number: 508 with model VECM in generation 3 of 5\n",
      "Model Number: 509 with model MetricMotif in generation 3 of 5\n",
      "Model Number: 510 with model MetricMotif in generation 3 of 5\n",
      "Model Number: 511 with model VAR in generation 3 of 5\n",
      "Model Number: 512 with model NVAR in generation 3 of 5\n",
      "Model Number: 513 with model WindowRegression in generation 3 of 5\n",
      "Model Number: 514 with model ETS in generation 3 of 5\n",
      "Model Number: 515 with model ConstantNaive in generation 3 of 5\n",
      "Model Number: 516 with model VECM in generation 3 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 516 in generation 3: VECM\n",
      "Model Number: 517 with model AverageValueNaive in generation 3 of 5\n",
      "Model Number: 518 with model FBProphet in generation 3 of 5\n",
      "Model Number: 519 with model Theta in generation 3 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_ridge.py:200: LinAlgWarning: Ill-conditioned matrix (rcond=4.37717e-25): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 520 with model SeasonalNaive in generation 3 of 5\n",
      "Model Number: 521 with model ETS in generation 3 of 5\n",
      "Model Number: 522 with model LastValueNaive in generation 3 of 5\n",
      "Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 522 in generation 3: LastValueNaive\n",
      "Model Number: 523 with model FFT in generation 3 of 5\n",
      "Model Number: 524 with model Theta in generation 3 of 5\n",
      "Model Number: 525 with model ConstantNaive in generation 3 of 5\n",
      "Model Number: 526 with model DatepartRegression in generation 3 of 5\n",
      "Model Number: 527 with model SectionalMotif in generation 3 of 5\n",
      "Model Number: 528 with model WindowRegression in generation 3 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 528 in generation 3: WindowRegression\n",
      "Model Number: 529 with model WindowRegression in generation 3 of 5\n",
      "Template Eval Error: ModuleNotFoundError(\"No module named 'lightgbm'\") in model 529 in generation 3: WindowRegression\n",
      "Model Number: 530 with model MAR in generation 3 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\tools\\percentile.py:47: RuntimeWarning: All-NaN slice encountered\n",
      "  max_val = np.nanmax(arr)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 531 with model Theta in generation 3 of 5\n",
      "Model Number: 532 with model FFT in generation 3 of 5\n",
      "Model Number: 533 with model ETS in generation 3 of 5\n",
      "Template Eval Error: Exception('Transformer HolidayTransformer failed on fit') in model 533 in generation 3: ETS\n",
      "Model Number: 534 with model VAR in generation 3 of 5\n",
      "Model Number: 535 with model VAR in generation 3 of 5\n",
      "Template Eval Error: LinAlgError('2-th leading minor of the array is not positive definite') in model 535 in generation 3: VAR\n",
      "Model Number: 536 with model Theta in generation 3 of 5\n",
      "Model Number: 537 with model Theta in generation 3 of 5\n",
      "Model Number: 538 with model ARIMA in generation 3 of 5\n",
      "Model Number: 539 with model GLS in generation 3 of 5\n",
      "Model Number: 540 with model MultivariateMotif in generation 3 of 5\n",
      "Model Number: 541 with model VAR in generation 3 of 5\n",
      "Model Number: 542 with model MetricMotif in generation 3 of 5\n",
      "Template Eval Error: Exception('Transformer DatepartRegression failed on inverse') in model 542 in generation 3: MetricMotif\n",
      "Model Number: 543 with model LastValueNaive in generation 3 of 5\n",
      "Model Number: 544 with model MAR in generation 3 of 5\n",
      "Model Number: 545 with model MultivariateMotif in generation 3 of 5\n",
      "Model Number: 546 with model ARIMA in generation 3 of 5\n",
      "Model Number: 547 with model LastValueNaive in generation 3 of 5\n",
      "Model Number: 548 with model GLM in generation 3 of 5\n",
      "Template Eval Error: ValueError('regression_type=user and no future_regressor passed') in model 548 in generation 3: GLM\n",
      "Model Number: 549 with model DatepartRegression in generation 3 of 5\n",
      "Model Number: 550 with model VECM in generation 3 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor supplied\") in model 550 in generation 3: VECM\n",
      "Model Number: 551 with model MultivariateMotif in generation 3 of 5\n",
      "Model Number: 552 with model LastValueNaive in generation 3 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 552 in generation 3: LastValueNaive\n",
      "Model Number: 553 with model VAR in generation 3 of 5\n",
      "Model Number: 554 with model FBProphet in generation 3 of 5\n",
      "Model Number: 555 with model MultivariateMotif in generation 3 of 5\n",
      "Model Number: 556 with model WindowRegression in generation 3 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\experimental\\enable_hist_gradient_boosting.py:15: UserWarning: Since version 1.0, it is not needed to import enable_hist_gradient_boosting anymore. HistGradientBoostingClassifier and HistGradientBoostingRegressor are now stable and can be normally imported from sklearn.ensemble.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 557 with model GLS in generation 3 of 5\n",
      "Model Number: 558 with model WindowRegression in generation 3 of 5\n",
      "Model Number: 559 with model ConstantNaive in generation 3 of 5\n",
      "Model Number: 560 with model SeasonalityMotif in generation 3 of 5\n",
      "Model Number: 561 with model ARIMA in generation 3 of 5\n",
      "Model Number: 562 with model MultivariateMotif in generation 3 of 5\n",
      "Model Number: 563 with model FBProphet in generation 3 of 5\n",
      "Model Number: 564 with model GLS in generation 3 of 5\n",
      "Model Number: 565 with model DatepartRegression in generation 3 of 5\n",
      "Model Number: 566 with model ARDL in generation 3 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but future_regressor not supplied\") in model 566 in generation 3: ARDL\n",
      "Model Number: 567 with model ARCH in generation 3 of 5\n",
      "Model Number: 568 with model LastValueNaive in generation 3 of 5\n",
      "Model Number: 569 with model VECM in generation 3 of 5\n",
      "Model Number: 570 with model SeasonalityMotif in generation 3 of 5\n",
      "Model Number: 571 with model LastValueNaive in generation 3 of 5\n",
      "Model Number: 572 with model SeasonalityMotif in generation 3 of 5\n",
      "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 572 in generation 3: SeasonalityMotif\n",
      "Model Number: 573 with model UnobservedComponents in generation 3 of 5\n",
      "Model Number: 574 with model ETS in generation 3 of 5\n",
      "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 574 in generation 3: ETS\n",
      "Model Number: 575 with model FBProphet in generation 3 of 5\n",
      "Model Number: 576 with model BallTreeMultivariateMotif in generation 3 of 5\n",
      "Model Number: 577 with model SectionalMotif in generation 3 of 5\n",
      "Model Number: 578 with model BallTreeMultivariateMotif in generation 3 of 5\n",
      "Model Number: 579 with model SectionalMotif in generation 3 of 5\n",
      "Model Number: 580 with model FFT in generation 3 of 5\n",
      "Model Number: 581 with model MAR in generation 3 of 5\n",
      "Model Number: 582 with model ARCH in generation 3 of 5\n",
      "Model Number: 583 with model ARCH in generation 3 of 5\n",
      "Model Number: 584 with model SeasonalNaive in generation 3 of 5\n",
      "Model Number: 585 with model SeasonalNaive in generation 3 of 5\n",
      "Model Number: 586 with model VAR in generation 3 of 5\n",
      "Model Number: 587 with model ETS in generation 3 of 5\n",
      "Model Number: 588 with model DatepartRegression in generation 3 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 588 in generation 3: DatepartRegression\n",
      "Model Number: 589 with model AverageValueNaive in generation 3 of 5\n",
      "Model Number: 590 with model SectionalMotif in generation 3 of 5\n",
      "Model Number: 591 with model UnivariateMotif in generation 3 of 5\n",
      "Model Number: 592 with model DatepartRegression in generation 3 of 5\n",
      "Model Number: 593 with model SectionalMotif in generation 3 of 5\n",
      "Model Number: 594 with model FBProphet in generation 3 of 5\n",
      "Model Number: 595 with model KalmanStateSpace in generation 3 of 5\n",
      "Model Number: 596 with model ETS in generation 3 of 5\n",
      "Model Number: 597 with model Theta in generation 3 of 5\n",
      "Model Number: 598 with model SeasonalityMotif in generation 3 of 5\n",
      "Model Number: 599 with model VAR in generation 3 of 5\n",
      "Model Number: 600 with model VECM in generation 3 of 5\n",
      "Model Number: 601 with model MAR in generation 3 of 5\n",
      "Model Number: 602 with model DatepartRegression in generation 3 of 5\n",
      "Model Number: 603 with model Cassandra in generation 3 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:2412: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 270030507.1339956, tolerance: 104644.38651695284\n",
      "  ) = cd_fast.enet_coordinate_descent_multi_task(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Template Eval Error: ValueError('window shape cannot be larger than input array shape') in model 603 in generation 3: Cassandra\n",
      "Model Number: 604 with model ConstantNaive in generation 3 of 5\n",
      "Model Number: 605 with model LastValueNaive in generation 3 of 5\n",
      "Model Number: 606 with model DatepartRegression in generation 3 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (250) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 607 with model UnobservedComponents in generation 3 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor supplied\") in model 607 in generation 3: UnobservedComponents\n",
      "Model Number: 608 with model VAR in generation 3 of 5\n",
      "Model Number: 609 with model WindowRegression in generation 3 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_ridge.py:200: LinAlgWarning: Ill-conditioned matrix (rcond=4.37717e-25): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 610 with model AverageValueNaive in generation 3 of 5\n",
      "Model Number: 611 with model MetricMotif in generation 3 of 5\n",
      "Model Number: 612 with model WindowRegression in generation 3 of 5\n",
      "Template Eval Error: ModuleNotFoundError(\"No module named 'lightgbm'\") in model 612 in generation 3: WindowRegression\n",
      "Model Number: 613 with model Theta in generation 3 of 5\n",
      "Model Number: 614 with model AverageValueNaive in generation 3 of 5\n",
      "Model Number: 615 with model SeasonalityMotif in generation 3 of 5\n",
      "Model Number: 616 with model SeasonalNaive in generation 3 of 5\n",
      "Model Number: 617 with model AverageValueNaive in generation 3 of 5\n",
      "Model Number: 618 with model ARIMA in generation 3 of 5\n",
      "Model Number: 619 with model ConstantNaive in generation 3 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 619 in generation 3: ConstantNaive\n",
      "Model Number: 620 with model Theta in generation 3 of 5\n",
      "Model Number: 621 with model UnivariateMotif in generation 3 of 5\n",
      "Model Number: 622 with model SeasonalNaive in generation 3 of 5\n",
      "Model Number: 623 with model UnobservedComponents in generation 3 of 5\n",
      "Model Number: 624 with model GLS in generation 3 of 5\n",
      "Model Number: 625 with model FFT in generation 3 of 5\n",
      "Model Number: 626 with model RRVAR in generation 3 of 5\n",
      "Model Number: 627 with model MultivariateMotif in generation 3 of 5\n",
      "Model Number: 628 with model DatepartRegression in generation 3 of 5\n",
      "Model Number: 629 with model SectionalMotif in generation 3 of 5\n",
      "Model Number: 630 with model SectionalMotif in generation 3 of 5\n",
      "Model Number: 631 with model ConstantNaive in generation 3 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:2412: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 127224504.41123235, tolerance: 106449.39024441625\n",
      "  ) = cd_fast.enet_coordinate_descent_multi_task(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 632 with model SectionalMotif in generation 3 of 5\n",
      "Model Number: 633 with model ConstantNaive in generation 3 of 5\n",
      "New Generation: 4 of 5\n",
      "Model Number: 634 with model UnivariateMotif in generation 4 of 5\n",
      "Model Number: 635 with model Cassandra in generation 4 of 5\n",
      "Model Number: 636 with model FFT in generation 4 of 5"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Number: 637 with model WindowRegression in generation 4 of 5\n",
      "Model Number: 638 with model GLM in generation 4 of 5\n",
      "Model Number: 639 with model WindowRegression in generation 4 of 5\n",
      "Model Number: 640 with model ETS in generation 4 of 5\n",
      "Model Number: 641 with model Theta in generation 4 of 5\n",
      "Model Number: 642 with model FFT in generation 4 of 5\n",
      "Model Number: 643 with model SeasonalityMotif in generation 4 of 5\n",
      "Model Number: 644 with model ARIMA in generation 4 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but future_regressor not supplied\") in model 644 in generation 4: ARIMA\n",
      "Model Number: 645 with model ETS in generation 4 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 645 in generation 4: ETS\n",
      "Model Number: 646 with model FFT in generation 4 of 5\n",
      "Model Number: 647 with model GLS in generation 4 of 5\n",
      "Model Number: 648 with model ARIMA in generation 4 of 5\n",
      "Model Number: 649 with model KalmanStateSpace in generation 4 of 5\n",
      "Model Number: 650 with model BallTreeMultivariateMotif in generation 4 of 5\n",
      "Model Number: 651 with model SectionalMotif in generation 4 of 5\n",
      "Model Number: 652 with model ETS in generation 4 of 5\n",
      "Model Number: 653 with model SeasonalNaive in generation 4 of 5\n",
      "Model Number: 654 with model ARIMA in generation 4 of 5\n",
      "Model Number: 655 with model DatepartRegression in generation 4 of 5\n",
      "Template Eval Error: ImportError('Tensorflow not available, install with pip install tensorflow.') in model 655 in generation 4: DatepartRegression\n",
      "Model Number: 656 with model SeasonalNaive in generation 4 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_ridge.py:200: LinAlgWarning: Ill-conditioned matrix (rcond=4.37717e-25): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 657 with model AverageValueNaive in generation 4 of 5\n",
      "Model Number: 658 with model Theta in generation 4 of 5\n",
      "Model Number: 659 with model ConstantNaive in generation 4 of 5\n",
      "Model Number: 660 with model MultivariateMotif in generation 4 of 5\n",
      "Model Number: 661 with model FBProphet in generation 4 of 5\n",
      "Model Number: 662 with model MultivariateMotif in generation 4 of 5\n",
      "Model Number: 663 with model ETS in generation 4 of 5\n",
      "Model Number: 664 with model MAR in generation 4 of 5\n",
      "Model Number: 665 with model Cassandra in generation 4 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 666 with model DatepartRegression in generation 4 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 666 in generation 4: DatepartRegression\n",
      "Model Number: 667 with model AverageValueNaive in generation 4 of 5\n",
      "Model Number: 668 with model WindowRegression in generation 4 of 5\n",
      "Template Eval Error: ModuleNotFoundError(\"No module named 'lightgbm'\") in model 668 in generation 4: WindowRegression\n",
      "Model Number: 669 with model ARDL in generation 4 of 5\n",
      "Model Number: 670 with model FBProphet in generation 4 of 5\n",
      "Model Number: 671 with model UnobservedComponents in generation 4 of 5\n",
      "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 671 in generation 4: UnobservedComponents\n",
      "Model Number: 672 with model RRVAR in generation 4 of 5\n",
      "Model Number: 673 with model SeasonalityMotif in generation 4 of 5\n",
      "Model Number: 674 with model ConstantNaive in generation 4 of 5\n",
      "Model Number: 675 with model BallTreeMultivariateMotif in generation 4 of 5\n",
      "Model Number: 676 with model VAR in generation 4 of 5\n",
      "Model Number: 677 with model ETS in generation 4 of 5\n",
      "Model Number: 678 with model ARCH in generation 4 of 5\n",
      "Template Eval Error: ValueError('p and q must be either 0 or 1.') in model 678 in generation 4: ARCH\n",
      "Model Number: 679 with model MultivariateMotif in generation 4 of 5\n",
      "Model Number: 680 with model FBProphet in generation 4 of 5\n",
      "Model Number: 681 with model ARIMA in generation 4 of 5\n",
      "Model Number: 682 with model Theta in generation 4 of 5\n",
      "Model Number: 683 with model ETS in generation 4 of 5\n",
      "Model Number: 684 with model VECM in generation 4 of 5\n",
      "Model Number: 685 with model ConstantNaive in generation 4 of 5\n",
      "Model Number: 686 with model KalmanStateSpace in generation 4 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:2412: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 270030507.1339956, tolerance: 104644.38651695284\n",
      "  ) = cd_fast.enet_coordinate_descent_multi_task(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 687 with model LastValueNaive in generation 4 of 5\n",
      "Model Number: 688 with model FBProphet in generation 4 of 5\n",
      "Model Number: 689 with model SeasonalityMotif in generation 4 of 5\n",
      "Model Number: 690 with model DatepartRegression in generation 4 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 690 in generation 4: DatepartRegression\n",
      "Model Number: 691 with model ARCH in generation 4 of 5\n",
      "Model Number: 692 with model Cassandra in generation 4 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 693 with model SeasonalityMotif in generation 4 of 5\n",
      "Model Number: 694 with model FBProphet in generation 4 of 5\n",
      "Model Number: 695 with model FBProphet in generation 4 of 5\n",
      "Model Number: 696 with model NVAR in generation 4 of 5\n",
      "Model Number: 697 with model ARIMA in generation 4 of 5\n",
      "Model Number: 698 with model NVAR in generation 4 of 5\n",
      "Model Number: 699 with model MultivariateMotif in generation 4 of 5\n",
      "Model Number: 700 with model GLS in generation 4 of 5\n",
      "Model Number: 701 with model VAR in generation 4 of 5\n",
      "Model Number: 702 with model FBProphet in generation 4 of 5\n",
      "Model Number: 703 with model UnivariateMotif in generation 4 of 5\n",
      "Model Number: 704 with model MetricMotif in generation 4 of 5\n",
      "Model Number: 705 with model SeasonalNaive in generation 4 of 5\n",
      "Model Number: 706 with model MAR in generation 4 of 5\n",
      "Template Eval Error: ValueError('Shape of passed values is (7, 5), indices imply (15, 5)') in model 706 in generation 4: MAR\n",
      "Model Number: 707 with model SeasonalityMotif in generation 4 of 5\n",
      "Model Number: 708 with model SeasonalNaive in generation 4 of 5\n",
      "Model Number: 709 with model LastValueNaive in generation 4 of 5\n",
      "Model Number: 710 with model GLS in generation 4 of 5\n",
      "Model Number: 711 with model VAR in generation 4 of 5\n",
      "Model Number: 712 with model LastValueNaive in generation 4 of 5\n",
      "Model Number: 713 with model Theta in generation 4 of 5\n",
      "Model Number: 714 with model WindowRegression in generation 4 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 714 in generation 4: WindowRegression\n",
      "Model Number: 715 with model MetricMotif in generation 4 of 5\n",
      "Model Number: 716 with model ETS in generation 4 of 5\n",
      "Model Number: 717 with model FFT in generation 4 of 5\n",
      "Model Number: 718 with model VAR in generation 4 of 5\n",
      "Model Number: 719 with model ARCH in generation 4 of 5\n",
      "Model Number: 720 with model Theta in generation 4 of 5\n",
      "Model Number: 721 with model SeasonalityMotif in generation 4 of 5\n",
      "Model Number: 722 with model MAR in generation 4 of 5\n",
      "Model Number: 723 with model UnobservedComponents in generation 4 of 5\n",
      "Model Number: 724 with model LastValueNaive in generation 4 of 5\n",
      "Model Number: 725 with model ARDL in generation 4 of 5\n",
      "Model Number: 726 with model UnobservedComponents in generation 4 of 5\n",
      "Model Number: 727 with model VECM in generation 4 of 5\n",
      "Model Number: 728 with model AverageValueNaive in generation 4 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 728 in generation 4: AverageValueNaive\n",
      "Model Number: 729 with model ConstantNaive in generation 4 of 5\n",
      "Model Number: 730 with model SectionalMotif in generation 4 of 5\n",
      "Model Number: 731 with model VECM in generation 4 of 5\n",
      "Model Number: 732 with model UnobservedComponents in generation 4 of 5\n",
      "Model Number: 733 with model VAR in generation 4 of 5\n",
      "Model Number: 734 with model VECM in generation 4 of 5\n",
      "Model Number: 735 with model GLS in generation 4 of 5\n",
      "Model Number: 736 with model FBProphet in generation 4 of 5\n",
      "Model Number: 737 with model VECM in generation 4 of 5\n",
      "Model Number: 738 with model SeasonalityMotif in generation 4 of 5\n",
      "Model Number: 739 with model AverageValueNaive in generation 4 of 5\n",
      "Model Number: 740 with model ARDL in generation 4 of 5\n",
      "Template Eval Error: ValueError(\"ARDL series A failed with error IndexError('tuple index out of range') exog train None and predict None\") in model 740 in generation 4: ARDL\n",
      "Model Number: 741 with model Cassandra in generation 4 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 742 with model FFT in generation 4 of 5\n",
      "Model Number: 743 with model SeasonalityMotif in generation 4 of 5\n",
      "Model Number: 744 with model WindowRegression in generation 4 of 5\n",
      "Model Number: 745 with model SectionalMotif in generation 4 of 5\n",
      "Model Number: 746 with model Cassandra in generation 4 of 5\n",
      "Model Number: 747 with model MetricMotif in generation 4 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 748 with model FBProphet in generation 4 of 5\n",
      "Model Number: 749 with model SectionalMotif in generation 4 of 5\n",
      "Model Number: 750 with model FBProphet in generation 4 of 5\n",
      "Model Number: 751 with model MetricMotif in generation 4 of 5\n",
      "Model Number: 752 with model MAR in generation 4 of 5\n",
      "Model Number: 753 with model UnivariateMotif in generation 4 of 5\n",
      "Model Number: 754 with model Theta in generation 4 of 5\n",
      "Model Number: 755 with model LastValueNaive in generation 4 of 5\n",
      "Model Number: 756 with model ARCH in generation 4 of 5\n",
      "Model Number: 757 with model VAR in generation 4 of 5\n",
      "Model Number: 758 with model Theta in generation 4 of 5\n",
      "Model Number: 759 with model MultivariateMotif in generation 4 of 5\n",
      "Model Number: 760 with model VAR in generation 4 of 5\n",
      "Model Number: 761 with model MAR in generation 4 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 761 in generation 4: MAR\n",
      "Model Number: 762 with model MultivariateMotif in generation 4 of 5\n",
      "Model Number: 763 with model Theta in generation 4 of 5\n",
      "Model Number: 764 with model VAR in generation 4 of 5\n",
      "Model Number: 765 with model VAR in generation 4 of 5\n",
      "Model Number: 766 with model FBProphet in generation 4 of 5\n",
      "Model Number: 767 with model WindowRegression in generation 4 of 5\n",
      "Template Eval Error: ImportError('Tensorflow not available, install with pip install tensorflow.') in model 767 in generation 4: WindowRegression\n",
      "Model Number: 768 with model MAR in generation 4 of 5\n",
      "Model Number: 769 with model ARCH in generation 4 of 5\n",
      "Model Number: 770 with model ARCH in generation 4 of 5\n",
      "Model Number: 771 with model VECM in generation 4 of 5\n",
      "Model Number: 772 with model VAR in generation 4 of 5\n",
      "Model Number: 773 with model ETS in generation 4 of 5\n",
      "Model Number: 774 with model ConstantNaive in generation 4 of 5\n",
      "Model Number: 775 with model Theta in generation 4 of 5\n",
      "Model Number: 776 with model UnivariateMotif in generation 4 of 5\n",
      "Model Number: 777 with model RRVAR in generation 4 of 5\n",
      "Model Number: 778 with model MAR in generation 4 of 5\n",
      "New Generation: 5 of 5\n",
      "Model Number: 779 with model SectionalMotif in generation 5 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 779 in generation 5: SectionalMotif\n",
      "Model Number: 780 with model SeasonalityMotif in generation 5 of 5\n",
      "Model Number: 781 with model MetricMotif in generation 5 of 5\n",
      "Model Number: 782 with model FFT in generation 5 of 5\n",
      "Model Number: 783 with model WindowRegression in generation 5 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 783 in generation 5: WindowRegression\n",
      "Model Number: 784 with model DatepartRegression in generation 5 of 5\n",
      "Model Number: 785 with model FBProphet in generation 5 of 5\n",
      "Model Number: 786 with model LastValueNaive in generation 5 of 5\n",
      "Model Number: 787 with model ARDL in generation 5 of 5\n",
      "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 787 in generation 5: ARDL\n",
      "Model Number: 788 with model MAR in generation 5 of 5\n",
      "Model Number: 789 with model LastValueNaive in generation 5 of 5\n",
      "Model Number: 790 with model MultivariateMotif in generation 5 of 5\n",
      "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 790 in generation 5: MultivariateMotif\n",
      "Model Number: 791 with model MAR in generation 5 of 5\n",
      "Template Eval Error: ValueError('Shape of passed values is (14, 5), indices imply (15, 5)') in model 791 in generation 5: MAR\n",
      "Model Number: 792 with model SeasonalNaive in generation 5 of 5\n",
      "Model Number: 793 with model UnobservedComponents in generation 5 of 5\n",
      "Model Number: 794 with model VECM in generation 5 of 5\n",
      "Model Number: 795 with model ARIMA in generation 5 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but future_regressor not supplied\") in model 795 in generation 5: ARIMA\n",
      "Model Number: 796 with model SeasonalityMotif in generation 5 of 5\n",
      "Model Number: 797 with model Cassandra in generation 5 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 798 with model FBProphet in generation 5 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 798 in generation 5: FBProphet\n",
      "Model Number: 799 with model MetricMotif in generation 5 of 5\n",
      "Model Number: 800 with model Theta in generation 5 of 5\n",
      "Model Number: 801 with model ARIMA in generation 5 of 5\n",
      "Model Number: 802 with model ARCH in generation 5 of 5\n",
      "Model Number: 803 with model MetricMotif in generation 5 of 5\n",
      "Model Number: 804 with model LastValueNaive in generation 5 of 5\n",
      "Model Number: 805 with model UnobservedComponents in generation 5 of 5\n",
      "Model Number: 806 with model ETS in generation 5 of 5\n",
      "Model Number: 807 with model RRVAR in generation 5 of 5\n",
      "Model Number: 808 with model MultivariateMotif in generation 5 of 5\n",
      "Model Number: 809 with model ARIMA in generation 5 of 5\n",
      "Model Number: 810 with model ARCH in generation 5 of 5\n",
      "Model Number: 811 with model MultivariateMotif in generation 5 of 5\n",
      "Model Number: 812 with model Theta in generation 5 of 5\n",
      "Template Eval Error: Exception('Transformer DatepartRegression failed on inverse') in model 812 in generation 5: Theta\n",
      "Model Number: 813 with model MetricMotif in generation 5 of 5\n",
      "Model Number: 814 with model DatepartRegression in generation 5 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (250) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\pandas\\core\\internals\\blocks.py:351: RuntimeWarning: overflow encountered in exp\n",
      "  result = func(self.values, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 815 with model VAR in generation 5 of 5\n",
      "Template Eval Error: ValueError(\"Model returned NaN due to a preprocessing transformer {'fillna': 'ffill', 'transformations': {'0': 'HPFilter', '1': 'bkfilter', '2': 'Log'}, 'transformation_params': {'0': {'part': 'trend', 'lamb': 129600}, '1': {}, '2': {}}}. fail_on_forecast_nan=True\") in model 815 in generation 5: VAR\n",
      "Model Number: 816 with model VAR in generation 5 of 5\n",
      "Template Eval Error: IndexError('index 0 is out of bounds for axis 0 with size 0') in model 816 in generation 5: VAR\n",
      "Model Number: 817 with model VAR in generation 5 of 5\n",
      "Model Number: 818 with model Cassandra in generation 5 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 819 with model FBProphet in generation 5 of 5\n",
      "Model Number: 820 with model FBProphet in generation 5 of 5\n",
      "Model Number: 821 with model VECM in generation 5 of 5\n",
      "Model Number: 822 with model SeasonalityMotif in generation 5 of 5\n",
      "Model Number: 823 with model VAR in generation 5 of 5\n",
      "Model Number: 824 with model ETS in generation 5 of 5\n",
      "Model Number: 825 with model RRVAR in generation 5 of 5\n",
      "Model Number: 826 with model ARIMA in generation 5 of 5\n",
      "Model Number: 827 with model Theta in generation 5 of 5\n",
      "Model Number: 828 with model AverageValueNaive in generation 5 of 5\n",
      "Model Number: 829 with model Theta in generation 5 of 5\n",
      "Model Number: 830 with model WindowRegression in generation 5 of 5\n",
      "Model Number: 831 with model ARCH in generation 5 of 5\n",
      "Model Number: 832 with model VECM in generation 5 of 5\n",
      "Model Number: 833 with model VAR in generation 5 of 5\n",
      "Template Eval Error: IndexError('index 0 is out of bounds for axis 0 with size 0') in model 833 in generation 5: VAR\n",
      "Model Number: 834 with model FBProphet in generation 5 of 5\n",
      "Model Number: 835 with model ETS in generation 5 of 5\n",
      "Model Number: 836 with model MultivariateMotif in generation 5 of 5\n",
      "Model Number: 837 with model ConstantNaive in generation 5 of 5\n",
      "Template Eval Error: ValueError(\"Model returned NaN due to a preprocessing transformer {'fillna': 'rolling_mean', 'transformations': {'0': 'AlignLastValue', '1': 'Slice', '2': 'Round', '3': 'AlignLastValue'}, 'transformation_params': {'0': {'rows': 1, 'lag': 2, 'method': 'additive', 'strength': 1.0, 'first_value_only': False}, '1': {'method': 0.5}, '2': {'decimals': -1, 'on_transform': False, 'on_inverse': True}, '3': {'rows': 1, 'lag': 1, 'method': 'multiplicative', 'strength': 1.0, 'first_value_only': False}}}. fail_on_forecast_nan=True\") in model 837 in generation 5: ConstantNaive\n",
      "Model Number: 838 with model SeasonalNaive in generation 5 of 5\n",
      "Model Number: 839 with model LastValueNaive in generation 5 of 5\n",
      "Model Number: 840 with model AverageValueNaive in generation 5 of 5\n",
      "Model Number: 841 with model GLS in generation 5 of 5\n",
      "Model Number: 842 with model MetricMotif in generation 5 of 5\n",
      "Model Number: 843 with model VAR in generation 5 of 5\n",
      "Model Number: 844 with model FBProphet in generation 5 of 5\n",
      "Model Number: 845 with model ARCH in generation 5 of 5\n",
      "Model Number: 846 with model VECM in generation 5 of 5\n",
      "Model Number: 847 with model FFT in generation 5 of 5\n",
      "Model Number: 848 with model ARIMA in generation 5 of 5\n",
      "Model Number: 849 with model ConstantNaive in generation 5 of 5\n",
      "Model Number: 850 with model Theta in generation 5 of 5\n",
      "Model Number: 851 with model ARIMA in generation 5 of 5\n",
      "Model Number: 852 with model FBProphet in generation 5 of 5\n",
      "Model Number: 853 with model ConstantNaive in generation 5 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 853 in generation 5: ConstantNaive\n",
      "Model Number: 854 with model MultivariateMotif in generation 5 of 5\n",
      "Model Number: 855 with model Theta in generation 5 of 5\n",
      "Model Number: 856 with model MetricMotif in generation 5 of 5\n",
      "Model Number: 857 with model AverageValueNaive in generation 5 of 5\n",
      "Model Number: 858 with model ETS in generation 5 of 5\n",
      "Model Number: 859 with model Theta in generation 5 of 5\n",
      "Model Number: 860 with model ConstantNaive in generation 5 of 5\n",
      "Model Number: 861 with model Cassandra in generation 5 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:2412: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 270030507.1339956, tolerance: 104644.38651695284\n",
      "  ) = cd_fast.enet_coordinate_descent_multi_task(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 861 in generation 5: Cassandra\n",
      "Model Number: 862 with model LastValueNaive in generation 5 of 5\n",
      "Model Number: 863 with model MetricMotif in generation 5 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 864 with model ARIMA in generation 5 of 5\n",
      "Model Number: 865 with model ARCH in generation 5 of 5\n",
      "Model Number: 866 with model WindowRegression in generation 5 of 5\n",
      "Model Number: 867 with model Cassandra in generation 5 of 5\n",
      "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 867 in generation 5: Cassandra\n",
      "Model Number: 868 with model MetricMotif in generation 5 of 5\n",
      "Model Number: 869 with model WindowRegression in generation 5 of 5\n",
      "Model Number: 870 with model FBProphet in generation 5 of 5\n",
      "Model Number: 871 with model SeasonalityMotif in generation 5 of 5\n",
      "Template Eval Error: Exception('Transformer DatepartRegression failed on inverse') in model 871 in generation 5: SeasonalityMotif\n",
      "Model Number: 872 with model SectionalMotif in generation 5 of 5\n",
      "Template Eval Error: Exception('Transformer ReplaceConstant failed on fit') in model 872 in generation 5: SectionalMotif\n",
      "Model Number: 873 with model LastValueNaive in generation 5 of 5\n",
      "Model Number: 874 with model ARDL in generation 5 of 5\n",
      "Template Eval Error: ValueError(\"ARDL series A failed with error IndexError('tuple index out of range') exog train None and predict None\") in model 874 in generation 5: ARDL\n",
      "Model Number: 875 with model ConstantNaive in generation 5 of 5\n",
      "Model Number: 876 with model FFT in generation 5 of 5\n",
      "Model Number: 877 with model SeasonalityMotif in generation 5 of 5\n",
      "Model Number: 878 with model ETS in generation 5 of 5\n",
      "Model Number: 879 with model MAR in generation 5 of 5\n",
      "Model Number: 880 with model MultivariateMotif in generation 5 of 5\n",
      "Model Number: 881 with model FBProphet in generation 5 of 5\n",
      "Model Number: 882 with model ETS in generation 5 of 5\n",
      "Model Number: 883 with model LastValueNaive in generation 5 of 5\n",
      "Model Number: 884 with model ARIMA in generation 5 of 5\n",
      "Model Number: 885 with model Cassandra in generation 5 of 5\n",
      "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 885 in generation 5: Cassandra\n",
      "Model Number: 886 with model Theta in generation 5 of 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Number: 887 with model Theta in generation 5 of 5\n",
      "Model Number: 888 with model ARCH in generation 5 of 5\n",
      "Model Number: 889 with model SectionalMotif in generation 5 of 5\n",
      "Model Number: 890 with model ETS in generation 5 of 5\n",
      "Model Number: 891 with model LastValueNaive in generation 5 of 5\n",
      "Model Number: 892 with model Theta in generation 5 of 5\n",
      "Model Number: 893 with model Theta in generation 5 of 5\n",
      "Model Number: 894 with model AverageValueNaive in generation 5 of 5\n",
      "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 894 in generation 5: AverageValueNaive\n",
      "Model Number: 895 with model Ensemble in generation 6 of Ensembles\n",
      "Model Number: 896 with model Ensemble in generation 6 of Ensembles\n",
      "Model Number: 897 with model Ensemble in generation 6 of Ensembles\n",
      "Model Number: 898 with model Ensemble in generation 6 of Ensembles\n",
      "Model Number: 899 with model Ensemble in generation 6 of Ensembles\n",
      "Model Number: 900 with model Ensemble in generation 6 of Ensembles\n",
      "Model Number: 901 with model Ensemble in generation 6 of Ensembles\n",
      "Model Number: 902 with model Ensemble in generation 6 of Ensembles\n",
      "Model Number: 903 with model Ensemble in generation 6 of Ensembles\n",
      "Model Number: 904 with model Ensemble in generation 6 of Ensembles\n",
      "Model Number: 905 with model Ensemble in generation 6 of Ensembles\n",
      "Model Number: 906 with model Ensemble in generation 6 of Ensembles\n",
      "Validation Round: 1\n",
      "Model Number: 1 of 146 with model Ensemble for Validation 1\n",
      "📈 1 - Ensemble with avg smape 9.41: \n",
      "Model Number: 2 of 146 with model Ensemble for Validation 1\n",
      "2 - Ensemble with avg smape 9.8: \n",
      "Model Number: 3 of 146 with model Ensemble for Validation 1\n",
      "3 - Ensemble with avg smape 10.01: \n",
      "Model Number: 4 of 146 with model Ensemble for Validation 1\n",
      "4 - Ensemble with avg smape 10.26: \n",
      "Model Number: 5 of 146 with model ARIMA for Validation 1\n",
      "5 - ARIMA with avg smape 11.2: \n",
      "Model Number: 6 of 146 with model FBProphet for Validation 1\n",
      "6 - FBProphet with avg smape 10.73: \n",
      "Model Number: 7 of 146 with model Ensemble for Validation 1\n",
      "7 - Ensemble with avg smape 10.67: \n",
      "Model Number: 8 of 146 with model FBProphet for Validation 1\n",
      "8 - FBProphet with avg smape 10.73: \n",
      "Model Number: 9 of 146 with model FBProphet for Validation 1\n",
      "9 - FBProphet with avg smape 10.68: \n",
      "Model Number: 10 of 146 with model Ensemble for Validation 1\n",
      "10 - Ensemble with avg smape 9.6: \n",
      "Model Number: 11 of 146 with model FBProphet for Validation 1\n",
      "11 - FBProphet with avg smape 10.86: \n",
      "Model Number: 12 of 146 with model FBProphet for Validation 1\n",
      "12 - FBProphet with avg smape 10.7: \n",
      "Model Number: 13 of 146 with model FBProphet for Validation 1\n",
      "13 - FBProphet with avg smape 10.62: \n",
      "Model Number: 14 of 146 with model ARIMA for Validation 1\n",
      "14 - ARIMA with avg smape 10.39: \n",
      "Model Number: 15 of 146 with model ARIMA for Validation 1\n",
      "15 - ARIMA with avg smape 10.39: \n",
      "Model Number: 16 of 146 with model LastValueNaive for Validation 1\n",
      "16 - LastValueNaive with avg smape 12.64: \n",
      "Model Number: 17 of 146 with model LastValueNaive for Validation 1\n",
      "17 - LastValueNaive with avg smape 12.62: \n",
      "Model Number: 18 of 146 with model LastValueNaive for Validation 1\n",
      "18 - LastValueNaive with avg smape 12.67: \n",
      "Model Number: 19 of 146 with model LastValueNaive for Validation 1\n",
      "19 - LastValueNaive with avg smape 12.52: \n",
      "Model Number: 20 of 146 with model LastValueNaive for Validation 1\n",
      "20 - LastValueNaive with avg smape 9.76: \n",
      "Model Number: 21 of 146 with model Theta for Validation 1\n",
      "21 - Theta with avg smape 9.93: \n",
      "Model Number: 22 of 146 with model LastValueNaive for Validation 1\n",
      "22 - LastValueNaive with avg smape 12.52: \n",
      "Model Number: 23 of 146 with model ARIMA for Validation 1\n",
      "23 - ARIMA with avg smape 10.15: \n",
      "Model Number: 24 of 146 with model Theta for Validation 1\n",
      "24 - Theta with avg smape 10.0: \n",
      "Model Number: 25 of 146 with model ARIMA for Validation 1\n",
      "25 - ARIMA with avg smape 12.7: \n",
      "Model Number: 26 of 146 with model VECM for Validation 1\n",
      "26 - VECM with avg smape 10.21: \n",
      "Model Number: 27 of 146 with model ETS for Validation 1\n",
      "27 - ETS with avg smape 10.08: \n",
      "Model Number: 28 of 146 with model ETS for Validation 1\n",
      "28 - ETS with avg smape 10.08: \n",
      "Model Number: 29 of 146 with model ETS for Validation 1\n",
      "29 - ETS with avg smape 10.19: \n",
      "Model Number: 30 of 146 with model Theta for Validation 1\n",
      "30 - Theta with avg smape 10.66: \n",
      "Model Number: 31 of 146 with model ETS for Validation 1\n",
      "31 - ETS with avg smape 11.72: \n",
      "Model Number: 32 of 146 with model ETS for Validation 1\n",
      "Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 32 in generation 0: ETS\n",
      "Model Number: 33 of 146 with model ETS for Validation 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\preprocessing\\_data.py:3308: RuntimeWarning: overflow encountered in power\n",
      "  out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33 - ETS with avg smape 10.07: \n",
      "Model Number: 34 of 146 with model ARIMA for Validation 1\n",
      "34 - ARIMA with avg smape 10.23: \n",
      "Model Number: 35 of 146 with model AverageValueNaive for Validation 1\n",
      "35 - AverageValueNaive with avg smape 10.51: \n",
      "Model Number: 36 of 146 with model Theta for Validation 1\n",
      "36 - Theta with avg smape 9.56: \n",
      "Model Number: 37 of 146 with model Theta for Validation 1\n",
      "📈 37 - Theta with avg smape 9.35: \n",
      "Model Number: 38 of 146 with model Theta for Validation 1\n",
      "38 - Theta with avg smape 9.65: \n",
      "Model Number: 39 of 146 with model MetricMotif for Validation 1\n",
      "39 - MetricMotif with avg smape 12.4: \n",
      "Model Number: 40 of 146 with model VECM for Validation 1\n",
      "40 - VECM with avg smape 9.52: \n",
      "Model Number: 41 of 146 with model SeasonalityMotif for Validation 1\n",
      "41 - SeasonalityMotif with avg smape 11.83: \n",
      "Model Number: 42 of 146 with model UnobservedComponents for Validation 1\n",
      "42 - UnobservedComponents with avg smape 10.59: \n",
      "Model Number: 43 of 146 with model SeasonalityMotif for Validation 1\n",
      "43 - SeasonalityMotif with avg smape 11.96: \n",
      "Model Number: 44 of 146 with model FFT for Validation 1\n",
      "📈 44 - FFT with avg smape 8.73: \n",
      "Model Number: 45 of 146 with model ARCH for Validation 1\n",
      "45 - ARCH with avg smape 10.51: \n",
      "Model Number: 46 of 146 with model ARCH for Validation 1\n",
      "46 - ARCH with avg smape 10.51: \n",
      "Model Number: 47 of 146 with model SeasonalityMotif for Validation 1\n",
      "47 - SeasonalityMotif with avg smape 13.33: \n",
      "Model Number: 48 of 146 with model SeasonalityMotif for Validation 1\n",
      "48 - SeasonalityMotif with avg smape 11.4: \n",
      "Model Number: 49 of 146 with model MetricMotif for Validation 1\n",
      "49 - MetricMotif with avg smape 10.64: \n",
      "Model Number: 50 of 146 with model MultivariateMotif for Validation 1\n",
      "50 - MultivariateMotif with avg smape 12.16: \n",
      "Model Number: 51 of 146 with model SeasonalityMotif for Validation 1\n",
      "51 - SeasonalityMotif with avg smape 14.59: \n",
      "Model Number: 52 of 146 with model ARDL for Validation 1\n",
      "52 - ARDL with avg smape 9.63: \n",
      "Model Number: 53 of 146 with model AverageValueNaive for Validation 1\n",
      "53 - AverageValueNaive with avg smape 9.61: \n",
      "Model Number: 54 of 146 with model AverageValueNaive for Validation 1\n",
      "54 - AverageValueNaive with avg smape 9.61: \n",
      "Model Number: 55 of 146 with model ARDL for Validation 1\n",
      "55 - ARDL with avg smape 9.63: \n",
      "Model Number: 56 of 146 with model MultivariateMotif for Validation 1\n",
      "56 - MultivariateMotif with avg smape 12.96: \n",
      "Model Number: 57 of 146 with model MetricMotif for Validation 1\n",
      "57 - MetricMotif with avg smape 10.98: \n",
      "Model Number: 58 of 146 with model KalmanStateSpace for Validation 1\n",
      "58 - KalmanStateSpace with avg smape 8.97: \n",
      "Model Number: 59 of 146 with model SectionalMotif for Validation 1\n",
      "59 - SectionalMotif with avg smape 9.77: \n",
      "Model Number: 60 of 146 with model VAR for Validation 1\n",
      "60 - VAR with avg smape 9.88: \n",
      "Model Number: 61 of 146 with model MetricMotif for Validation 1\n",
      "61 - MetricMotif with avg smape 8.93: \n",
      "Model Number: 62 of 146 with model MetricMotif for Validation 1\n",
      "62 - MetricMotif with avg smape 9.57: \n",
      "Model Number: 63 of 146 with model VECM for Validation 1\n",
      "63 - VECM with avg smape 9.88: \n",
      "Model Number: 64 of 146 with model AverageValueNaive for Validation 1\n",
      "64 - AverageValueNaive with avg smape 9.38: \n",
      "Model Number: 65 of 146 with model GLS for Validation 1\n",
      "65 - GLS with avg smape 10.54: \n",
      "Model Number: 66 of 146 with model VECM for Validation 1\n",
      "66 - VECM with avg smape 8.85: \n",
      "Model Number: 67 of 146 with model Cassandra for Validation 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67 - Cassandra with avg smape 12.93: \n",
      "Model Number: 68 of 146 with model VAR for Validation 1\n",
      "68 - VAR with avg smape 10.28: \n",
      "Model Number: 69 of 146 with model MultivariateMotif for Validation 1\n",
      "69 - MultivariateMotif with avg smape 12.32: \n",
      "Model Number: 70 of 146 with model MultivariateMotif for Validation 1\n",
      "70 - MultivariateMotif with avg smape 12.76: \n",
      "Model Number: 71 of 146 with model SeasonalNaive for Validation 1\n",
      "71 - SeasonalNaive with avg smape 14.58: \n",
      "Model Number: 72 of 146 with model ARCH for Validation 1\n",
      "72 - ARCH with avg smape 10.32: \n",
      "Model Number: 73 of 146 with model VAR for Validation 1\n",
      "73 - VAR with avg smape 8.73: \n",
      "Model Number: 74 of 146 with model VAR for Validation 1\n",
      "74 - VAR with avg smape 8.73: \n",
      "Model Number: 75 of 146 with model VAR for Validation 1\n",
      "75 - VAR with avg smape 8.73: \n",
      "Model Number: 76 of 146 with model VAR for Validation 1\n",
      "76 - VAR with avg smape 8.73: \n",
      "Model Number: 77 of 146 with model SeasonalNaive for Validation 1\n",
      "77 - SeasonalNaive with avg smape 14.7: \n",
      "Model Number: 78 of 146 with model VECM for Validation 1\n",
      "78 - VECM with avg smape 9.68: \n",
      "Model Number: 79 of 146 with model Cassandra for Validation 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79 - Cassandra with avg smape 10.89: \n",
      "Model Number: 80 of 146 with model WindowRegression for Validation 1\n",
      "80 - WindowRegression with avg smape 19.41: \n",
      "Model Number: 81 of 146 with model MultivariateMotif for Validation 1\n",
      "81 - MultivariateMotif with avg smape 12.25: \n",
      "Model Number: 82 of 146 with model ConstantNaive for Validation 1\n",
      "82 - ConstantNaive with avg smape 10.53: \n",
      "Model Number: 83 of 146 with model ARCH for Validation 1\n",
      "83 - ARCH with avg smape 10.31: \n",
      "Model Number: 84 of 146 with model AverageValueNaive for Validation 1\n",
      "84 - AverageValueNaive with avg smape 10.53: \n",
      "Model Number: 85 of 146 with model ConstantNaive for Validation 1\n",
      "85 - ConstantNaive with avg smape 10.53: \n",
      "Model Number: 86 of 146 with model DatepartRegression for Validation 1\n",
      "86 - DatepartRegression with avg smape 10.51: \n",
      "Model Number: 87 of 146 with model VECM for Validation 1\n",
      "87 - VECM with avg smape 10.51: \n",
      "Model Number: 88 of 146 with model UnivariateMotif for Validation 1\n",
      "88 - UnivariateMotif with avg smape 10.51: \n",
      "Model Number: 89 of 146 with model ConstantNaive for Validation 1\n",
      "89 - ConstantNaive with avg smape 10.51: \n",
      "Model Number: 90 of 146 with model ConstantNaive for Validation 1\n",
      "90 - ConstantNaive with avg smape 10.51: \n",
      "Model Number: 91 of 146 with model ConstantNaive for Validation 1\n",
      "91 - ConstantNaive with avg smape 10.51: \n",
      "Model Number: 92 of 146 with model SeasonalNaive for Validation 1\n",
      "92 - SeasonalNaive with avg smape 14.54: \n",
      "Model Number: 93 of 146 with model ARCH for Validation 1\n",
      "93 - ARCH with avg smape 9.56: \n",
      "Model Number: 94 of 146 with model MAR for Validation 1\n",
      "94 - MAR with avg smape 19.71: \n",
      "Model Number: 95 of 146 with model ARCH for Validation 1\n",
      "95 - ARCH with avg smape 10.11: \n",
      "Model Number: 96 of 146 with model AverageValueNaive for Validation 1\n",
      "96 - AverageValueNaive with avg smape 10.51: \n",
      "Model Number: 97 of 146 with model UnobservedComponents for Validation 1\n",
      "97 - UnobservedComponents with avg smape 10.51: \n",
      "Model Number: 98 of 146 with model UnobservedComponents for Validation 1\n",
      "98 - UnobservedComponents with avg smape 10.51: \n",
      "Model Number: 99 of 146 with model SectionalMotif for Validation 1\n",
      "99 - SectionalMotif with avg smape 11.54: \n",
      "Model Number: 100 of 146 with model FFT for Validation 1\n",
      "100 - FFT with avg smape 16.14: \n",
      "Model Number: 101 of 146 with model WindowRegression for Validation 1\n",
      "101 - WindowRegression with avg smape 10.75: \n",
      "Model Number: 102 of 146 with model ConstantNaive for Validation 1\n",
      "102 - ConstantNaive with avg smape 10.1: \n",
      "Model Number: 103 of 146 with model WindowRegression for Validation 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:2412: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 68667744.958386, tolerance: 90747.6186057731\n",
      "  ) = cd_fast.enet_coordinate_descent_multi_task(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103 - WindowRegression with avg smape 9.38: \n",
      "Model Number: 104 of 146 with model MultivariateMotif for Validation 1\n",
      "104 - MultivariateMotif with avg smape 9.38: \n",
      "Model Number: 105 of 146 with model SeasonalityMotif for Validation 1\n",
      "105 - SeasonalityMotif with avg smape 12.29: \n",
      "Model Number: 106 of 146 with model SectionalMotif for Validation 1\n",
      "106 - SectionalMotif with avg smape 11.16: \n",
      "Model Number: 107 of 146 with model GLS for Validation 1\n",
      "107 - GLS with avg smape 11.87: \n",
      "Model Number: 108 of 146 with model UnobservedComponents for Validation 1\n",
      "108 - UnobservedComponents with avg smape 9.38: \n",
      "Model Number: 109 of 146 with model FFT for Validation 1\n",
      "109 - FFT with avg smape 13.42: \n",
      "Model Number: 110 of 146 with model GLS for Validation 1\n",
      "110 - GLS with avg smape 12.25: \n",
      "Model Number: 111 of 146 with model SectionalMotif for Validation 1\n",
      "111 - SectionalMotif with avg smape 14.8: \n",
      "Model Number: 112 of 146 with model WindowRegression for Validation 1\n",
      "112 - WindowRegression with avg smape 10.55: \n",
      "Model Number: 113 of 146 with model RRVAR for Validation 1\n",
      "113 - RRVAR with avg smape 11.11: \n",
      "Model Number: 114 of 146 with model ARDL for Validation 1\n",
      "114 - ARDL with avg smape 8.99: \n",
      "Model Number: 115 of 146 with model GLS for Validation 1\n",
      "115 - GLS with avg smape 10.53: \n",
      "Model Number: 116 of 146 with model GLS for Validation 1\n",
      "116 - GLS with avg smape 10.53: \n",
      "Model Number: 117 of 146 with model SeasonalNaive for Validation 1\n",
      "117 - SeasonalNaive with avg smape 10.15: \n",
      "Model Number: 118 of 146 with model WindowRegression for Validation 1\n",
      "118 - WindowRegression with avg smape 10.34: \n",
      "Model Number: 119 of 146 with model SectionalMotif for Validation 1\n",
      "119 - SectionalMotif with avg smape 11.27: \n",
      "Model Number: 120 of 146 with model DatepartRegression for Validation 1\n",
      "120 - DatepartRegression with avg smape 9.59: \n",
      "Model Number: 121 of 146 with model GLS for Validation 1\n",
      "121 - GLS with avg smape 10.57: \n",
      "Model Number: 122 of 146 with model MetricMotif for Validation 1\n",
      "📈 122 - MetricMotif with avg smape 8.52: \n",
      "Model Number: 123 of 146 with model RRVAR for Validation 1\n",
      "123 - RRVAR with avg smape 10.8: \n",
      "Model Number: 124 of 146 with model FFT for Validation 1\n",
      "124 - FFT with avg smape 11.71: \n",
      "Model Number: 125 of 146 with model Cassandra for Validation 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125 - Cassandra with avg smape 11.96: \n",
      "Model Number: 126 of 146 with model UnivariateMotif for Validation 1\n",
      "126 - UnivariateMotif with avg smape 9.83: \n",
      "Model Number: 127 of 146 with model WindowRegression for Validation 1\n",
      "127 - WindowRegression with avg smape 9.68: \n",
      "Model Number: 128 of 146 with model FFT for Validation 1\n",
      "128 - FFT with avg smape 15.81: \n",
      "Model Number: 129 of 146 with model FFT for Validation 1\n",
      "129 - FFT with avg smape 15.81: \n",
      "Model Number: 130 of 146 with model UnobservedComponents for Validation 1\n",
      "130 - UnobservedComponents with avg smape 10.51: \n",
      "Model Number: 131 of 146 with model UnobservedComponents for Validation 1\n",
      "131 - UnobservedComponents with avg smape 10.51: \n",
      "Model Number: 132 of 146 with model MAR for Validation 1\n",
      "132 - MAR with avg smape 12.23: \n",
      "Model Number: 133 of 146 with model DatepartRegression for Validation 1\n",
      "133 - DatepartRegression with avg smape 11.57: \n",
      "Model Number: 134 of 146 with model Cassandra for Validation 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "134 - Cassandra with avg smape 11.47: \n",
      "Model Number: 135 of 146 with model MAR for Validation 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\tools\\percentile.py:47: RuntimeWarning: All-NaN slice encountered\n",
      "  max_val = np.nanmax(arr)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "135 - MAR with avg smape 14.22: \n",
      "Model Number: 136 of 146 with model SeasonalNaive for Validation 1\n",
      "136 - SeasonalNaive with avg smape 26.11: \n",
      "Model Number: 137 of 146 with model AverageValueNaive for Validation 1\n",
      "137 - AverageValueNaive with avg smape 9.61: \n",
      "Model Number: 138 of 146 with model Theta for Validation 1\n",
      "138 - Theta with avg smape 9.47: \n",
      "Model Number: 139 of 146 with model VAR for Validation 1\n",
      "139 - VAR with avg smape 10.93: \n",
      "Model Number: 140 of 146 with model FBProphet for Validation 1\n",
      "140 - FBProphet with avg smape 10.71: \n",
      "Model Number: 141 of 146 with model FBProphet for Validation 1\n",
      "141 - FBProphet with avg smape 10.71: \n",
      "Model Number: 142 of 146 with model FBProphet for Validation 1\n",
      "142 - FBProphet with avg smape 9.52: \n",
      "Model Number: 143 of 146 with model Theta for Validation 1\n",
      "143 - Theta with avg smape 10.93: \n",
      "Model Number: 144 of 146 with model MultivariateMotif for Validation 1\n",
      "144 - MultivariateMotif with avg smape 11.98: \n",
      "Model Number: 145 of 146 with model AverageValueNaive for Validation 1\n",
      "145 - AverageValueNaive with avg smape 13.75: \n",
      "Model Number: 146 of 146 with model FBProphet for Validation 1\n",
      "146 - FBProphet with avg smape 19.42: \n",
      "Validation Round: 2\n",
      "Model Number: 1 of 146 with model Ensemble for Validation 2\n",
      "📈 1 - Ensemble with avg smape 14.32: \n",
      "Model Number: 2 of 146 with model Ensemble for Validation 2\n",
      "2 - Ensemble with avg smape 14.48: \n",
      "Model Number: 3 of 146 with model Ensemble for Validation 2\n",
      "3 - Ensemble with avg smape 16.37: \n",
      "Model Number: 4 of 146 with model Ensemble for Validation 2\n",
      "📈 4 - Ensemble with avg smape 14.24: \n",
      "Model Number: 5 of 146 with model ARIMA for Validation 2\n",
      "5 - ARIMA with avg smape 14.56: \n",
      "Model Number: 6 of 146 with model FBProphet for Validation 2\n",
      "6 - FBProphet with avg smape 14.45: \n",
      "Model Number: 7 of 146 with model Ensemble for Validation 2\n",
      "7 - Ensemble with avg smape 14.46: \n",
      "Model Number: 8 of 146 with model FBProphet for Validation 2\n",
      "8 - FBProphet with avg smape 14.45: \n",
      "Model Number: 9 of 146 with model FBProphet for Validation 2\n",
      "9 - FBProphet with avg smape 14.52: \n",
      "Model Number: 10 of 146 with model Ensemble for Validation 2\n",
      "📈 10 - Ensemble with avg smape 13.81: \n",
      "Model Number: 11 of 146 with model FBProphet for Validation 2\n",
      "11 - FBProphet with avg smape 14.21: \n",
      "Model Number: 12 of 146 with model FBProphet for Validation 2\n",
      "12 - FBProphet with avg smape 14.45: \n",
      "Model Number: 13 of 146 with model FBProphet for Validation 2\n",
      "13 - FBProphet with avg smape 14.4: \n",
      "Model Number: 14 of 146 with model ARIMA for Validation 2\n",
      "14 - ARIMA with avg smape 15.9: \n",
      "Model Number: 15 of 146 with model ARIMA for Validation 2\n",
      "15 - ARIMA with avg smape 15.9: \n",
      "Model Number: 16 of 146 with model LastValueNaive for Validation 2\n",
      "16 - LastValueNaive with avg smape 20.28: \n",
      "Model Number: 17 of 146 with model LastValueNaive for Validation 2\n",
      "17 - LastValueNaive with avg smape 20.28: \n",
      "Model Number: 18 of 146 with model LastValueNaive for Validation 2\n",
      "18 - LastValueNaive with avg smape 20.21: \n",
      "Model Number: 19 of 146 with model LastValueNaive for Validation 2\n",
      "19 - LastValueNaive with avg smape 18.72: \n",
      "Model Number: 20 of 146 with model LastValueNaive for Validation 2\n",
      "📈 20 - LastValueNaive with avg smape 12.76: \n",
      "Model Number: 21 of 146 with model Theta for Validation 2\n",
      "21 - Theta with avg smape 13.01: \n",
      "Model Number: 22 of 146 with model LastValueNaive for Validation 2\n",
      "22 - LastValueNaive with avg smape 18.72: \n",
      "Model Number: 23 of 146 with model ARIMA for Validation 2\n",
      "23 - ARIMA with avg smape 15.42: \n",
      "Model Number: 24 of 146 with model Theta for Validation 2\n",
      "📈 24 - Theta with avg smape 12.19: \n",
      "Model Number: 25 of 146 with model ARIMA for Validation 2\n",
      "25 - ARIMA with avg smape 13.0: \n",
      "Model Number: 26 of 146 with model VECM for Validation 2\n",
      "26 - VECM with avg smape 13.11: \n",
      "Model Number: 27 of 146 with model ETS for Validation 2\n",
      "27 - ETS with avg smape 17.1: \n",
      "Model Number: 28 of 146 with model ETS for Validation 2\n",
      "28 - ETS with avg smape 17.09: \n",
      "Model Number: 29 of 146 with model ETS for Validation 2\n",
      "29 - ETS with avg smape 17.47: \n",
      "Model Number: 30 of 146 with model Theta for Validation 2\n",
      "30 - Theta with avg smape 14.68: \n",
      "Model Number: 31 of 146 with model ETS for Validation 2\n",
      "31 - ETS with avg smape 18.99: \n",
      "Model Number: 32 of 146 with model ETS for Validation 2\n",
      "32 - ETS with avg smape 23.7: \n",
      "Model Number: 33 of 146 with model ETS for Validation 2\n",
      "33 - ETS with avg smape 18.1: \n",
      "Model Number: 34 of 146 with model ARIMA for Validation 2\n",
      "34 - ARIMA with avg smape 21.62: \n",
      "Model Number: 35 of 146 with model AverageValueNaive for Validation 2\n",
      "35 - AverageValueNaive with avg smape 15.34: \n",
      "Model Number: 36 of 146 with model Theta for Validation 2\n",
      "📈 36 - Theta with avg smape 11.31: \n",
      "Model Number: 37 of 146 with model Theta for Validation 2\n",
      "37 - Theta with avg smape 11.77: \n",
      "Model Number: 38 of 146 with model Theta for Validation 2\n",
      "38 - Theta with avg smape 11.75: \n",
      "Model Number: 39 of 146 with model MetricMotif for Validation 2\n",
      "39 - MetricMotif with avg smape 14.76: \n",
      "Model Number: 40 of 146 with model VECM for Validation 2\n",
      "40 - VECM with avg smape 17.31: \n",
      "Model Number: 41 of 146 with model SeasonalityMotif for Validation 2\n",
      "41 - SeasonalityMotif with avg smape 12.59: \n",
      "Model Number: 42 of 146 with model UnobservedComponents for Validation 2\n",
      "📈 42 - UnobservedComponents with avg smape 10.45: \n",
      "Model Number: 43 of 146 with model SeasonalityMotif for Validation 2\n",
      "43 - SeasonalityMotif with avg smape 11.1: \n",
      "Model Number: 44 of 146 with model FFT for Validation 2\n",
      "44 - FFT with avg smape 14.05: \n",
      "Model Number: 45 of 146 with model ARCH for Validation 2\n",
      "45 - ARCH with avg smape 15.34: \n",
      "Model Number: 46 of 146 with model ARCH for Validation 2\n",
      "46 - ARCH with avg smape 15.34: \n",
      "Model Number: 47 of 146 with model SeasonalityMotif for Validation 2\n",
      "47 - SeasonalityMotif with avg smape 13.01: \n",
      "Model Number: 48 of 146 with model SeasonalityMotif for Validation 2\n",
      "48 - SeasonalityMotif with avg smape 11.8: \n",
      "Model Number: 49 of 146 with model MetricMotif for Validation 2\n",
      "49 - MetricMotif with avg smape 15.29: \n",
      "Model Number: 50 of 146 with model MultivariateMotif for Validation 2\n",
      "50 - MultivariateMotif with avg smape 17.31: \n",
      "Model Number: 51 of 146 with model SeasonalityMotif for Validation 2\n",
      "📈 51 - SeasonalityMotif with avg smape 8.82: \n",
      "Model Number: 52 of 146 with model ARDL for Validation 2\n",
      "📈 52 - ARDL with avg smape 8.66: \n",
      "Model Number: 53 of 146 with model AverageValueNaive for Validation 2\n",
      "53 - AverageValueNaive with avg smape 10.7: \n",
      "Model Number: 54 of 146 with model AverageValueNaive for Validation 2\n",
      "54 - AverageValueNaive with avg smape 10.7: \n",
      "Model Number: 55 of 146 with model ARDL for Validation 2\n",
      "55 - ARDL with avg smape 8.66: \n",
      "Model Number: 56 of 146 with model MultivariateMotif for Validation 2\n",
      "56 - MultivariateMotif with avg smape 18.34: \n",
      "Model Number: 57 of 146 with model MetricMotif for Validation 2\n",
      "57 - MetricMotif with avg smape 16.89: \n",
      "Model Number: 58 of 146 with model KalmanStateSpace for Validation 2\n",
      "58 - KalmanStateSpace with avg smape 14.05: \n",
      "Model Number: 59 of 146 with model SectionalMotif for Validation 2\n",
      "59 - SectionalMotif with avg smape 9.94: \n",
      "Model Number: 60 of 146 with model VAR for Validation 2\n",
      "60 - VAR with avg smape 17.2: \n",
      "Model Number: 61 of 146 with model MetricMotif for Validation 2\n",
      "61 - MetricMotif with avg smape 12.68: \n",
      "Model Number: 62 of 146 with model MetricMotif for Validation 2\n",
      "62 - MetricMotif with avg smape 13.83: \n",
      "Model Number: 63 of 146 with model VECM for Validation 2\n",
      "63 - VECM with avg smape 13.6: \n",
      "Model Number: 64 of 146 with model AverageValueNaive for Validation 2\n",
      "64 - AverageValueNaive with avg smape 12.21: \n",
      "Model Number: 65 of 146 with model GLS for Validation 2\n",
      "65 - GLS with avg smape 15.27: \n",
      "Model Number: 66 of 146 with model VECM for Validation 2\n",
      "66 - VECM with avg smape 10.83: \n",
      "Model Number: 67 of 146 with model Cassandra for Validation 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67 - Cassandra with avg smape 14.22: \n",
      "Model Number: 68 of 146 with model VAR for Validation 2\n",
      "68 - VAR with avg smape 18.79: \n",
      "Model Number: 69 of 146 with model MultivariateMotif for Validation 2\n",
      "69 - MultivariateMotif with avg smape 17.87: \n",
      "Model Number: 70 of 146 with model MultivariateMotif for Validation 2\n",
      "70 - MultivariateMotif with avg smape 17.49: \n",
      "Model Number: 71 of 146 with model SeasonalNaive for Validation 2\n",
      "71 - SeasonalNaive with avg smape 18.3: \n",
      "Model Number: 72 of 146 with model ARCH for Validation 2\n",
      "72 - ARCH with avg smape 14.67: \n",
      "Model Number: 73 of 146 with model VAR for Validation 2\n",
      "73 - VAR with avg smape 11.09: \n",
      "Model Number: 74 of 146 with model VAR for Validation 2\n",
      "74 - VAR with avg smape 11.09: \n",
      "Model Number: 75 of 146 with model VAR for Validation 2\n",
      "75 - VAR with avg smape 11.09: \n",
      "Model Number: 76 of 146 with model VAR for Validation 2\n",
      "76 - VAR with avg smape 11.09: \n",
      "Model Number: 77 of 146 with model SeasonalNaive for Validation 2\n",
      "77 - SeasonalNaive with avg smape 19.08: \n",
      "Model Number: 78 of 146 with model VECM for Validation 2\n",
      "78 - VECM with avg smape 13.97: \n",
      "Model Number: 79 of 146 with model Cassandra for Validation 2\n",
      "79 - Cassandra with avg smape 18.06: \n",
      "Model Number: 80 of 146 with model WindowRegression for Validation 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80 - WindowRegression with avg smape 23.84: \n",
      "Model Number: 81 of 146 with model MultivariateMotif for Validation 2\n",
      "81 - MultivariateMotif with avg smape 17.95: \n",
      "Model Number: 82 of 146 with model ConstantNaive for Validation 2\n",
      "82 - ConstantNaive with avg smape 15.29: \n",
      "Model Number: 83 of 146 with model ARCH for Validation 2\n",
      "83 - ARCH with avg smape 13.35: \n",
      "Model Number: 84 of 146 with model AverageValueNaive for Validation 2\n",
      "84 - AverageValueNaive with avg smape 15.29: \n",
      "Model Number: 85 of 146 with model ConstantNaive for Validation 2\n",
      "85 - ConstantNaive with avg smape 15.29: \n",
      "Model Number: 86 of 146 with model DatepartRegression for Validation 2\n",
      "86 - DatepartRegression with avg smape 15.34: \n",
      "Model Number: 87 of 146 with model VECM for Validation 2\n",
      "87 - VECM with avg smape 15.34: \n",
      "Model Number: 88 of 146 with model UnivariateMotif for Validation 2\n",
      "88 - UnivariateMotif with avg smape 15.34: \n",
      "Model Number: 89 of 146 with model ConstantNaive for Validation 2\n",
      "89 - ConstantNaive with avg smape 15.34: \n",
      "Model Number: 90 of 146 with model ConstantNaive for Validation 2\n",
      "90 - ConstantNaive with avg smape 15.34: \n",
      "Model Number: 91 of 146 with model ConstantNaive for Validation 2\n",
      "91 - ConstantNaive with avg smape 15.34: \n",
      "Model Number: 92 of 146 with model SeasonalNaive for Validation 2\n",
      "92 - SeasonalNaive with avg smape 18.2: \n",
      "Model Number: 93 of 146 with model ARCH for Validation 2\n",
      "93 - ARCH with avg smape 149.78: \n",
      "Model Number: 94 of 146 with model MAR for Validation 2\n",
      "94 - MAR with avg smape 28.04: \n",
      "Model Number: 95 of 146 with model ARCH for Validation 2\n",
      "95 - ARCH with avg smape 13.27: \n",
      "Model Number: 96 of 146 with model AverageValueNaive for Validation 2\n",
      "96 - AverageValueNaive with avg smape 15.34: \n",
      "Model Number: 97 of 146 with model UnobservedComponents for Validation 2\n",
      "97 - UnobservedComponents with avg smape 15.08: \n",
      "Model Number: 98 of 146 with model UnobservedComponents for Validation 2\n",
      "98 - UnobservedComponents with avg smape 18.22: \n",
      "Model Number: 99 of 146 with model SectionalMotif for Validation 2\n",
      "99 - SectionalMotif with avg smape 9.99: \n",
      "Model Number: 100 of 146 with model FFT for Validation 2\n",
      "100 - FFT with avg smape 14.59: \n",
      "Model Number: 101 of 146 with model WindowRegression for Validation 2\n",
      "101 - WindowRegression with avg smape 11.09: \n",
      "Model Number: 102 of 146 with model ConstantNaive for Validation 2\n",
      "102 - ConstantNaive with avg smape 12.21: \n",
      "Model Number: 103 of 146 with model WindowRegression for Validation 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:2412: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14631871.596238121, tolerance: 20308.2163088733\n",
      "  ) = cd_fast.enet_coordinate_descent_multi_task(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103 - WindowRegression with avg smape 12.21: \n",
      "Model Number: 104 of 146 with model MultivariateMotif for Validation 2\n",
      "104 - MultivariateMotif with avg smape 12.21: \n",
      "Model Number: 105 of 146 with model SeasonalityMotif for Validation 2\n",
      "105 - SeasonalityMotif with avg smape 12.18: \n",
      "Model Number: 106 of 146 with model SectionalMotif for Validation 2\n",
      "106 - SectionalMotif with avg smape 10.22: \n",
      "Model Number: 107 of 146 with model GLS for Validation 2\n",
      "107 - GLS with avg smape 12.3: \n",
      "Model Number: 108 of 146 with model UnobservedComponents for Validation 2\n",
      "108 - UnobservedComponents with avg smape 15.41: \n",
      "Model Number: 109 of 146 with model FFT for Validation 2\n",
      "109 - FFT with avg smape 18.34: \n",
      "Model Number: 110 of 146 with model GLS for Validation 2\n",
      "110 - GLS with avg smape 10.11: \n",
      "Model Number: 111 of 146 with model SectionalMotif for Validation 2\n",
      "111 - SectionalMotif with avg smape 8.76: \n",
      "Model Number: 112 of 146 with model WindowRegression for Validation 2\n",
      "📈 112 - WindowRegression with avg smape 7.76: \n",
      "Model Number: 113 of 146 with model RRVAR for Validation 2\n",
      "113 - RRVAR with avg smape 9.86: \n",
      "Model Number: 114 of 146 with model ARDL for Validation 2\n",
      "114 - ARDL with avg smape 11.88: \n",
      "Model Number: 115 of 146 with model GLS for Validation 2\n",
      "115 - GLS with avg smape 15.14: \n",
      "Model Number: 116 of 146 with model GLS for Validation 2\n",
      "116 - GLS with avg smape 15.14: \n",
      "Model Number: 117 of 146 with model SeasonalNaive for Validation 2\n",
      "117 - SeasonalNaive with avg smape 14.56: \n",
      "Model Number: 118 of 146 with model WindowRegression for Validation 2\n",
      "📈 118 - WindowRegression with avg smape 7.7: \n",
      "Model Number: 119 of 146 with model SectionalMotif for Validation 2\n",
      "119 - SectionalMotif with avg smape 9.47: \n",
      "Model Number: 120 of 146 with model DatepartRegression for Validation 2\n",
      "120 - DatepartRegression with avg smape 18.55: \n",
      "Model Number: 121 of 146 with model GLS for Validation 2\n",
      "121 - GLS with avg smape 15.2: \n",
      "Model Number: 122 of 146 with model MetricMotif for Validation 2\n",
      "122 - MetricMotif with avg smape 13.32: \n",
      "Model Number: 123 of 146 with model RRVAR for Validation 2\n",
      "123 - RRVAR with avg smape 10.05: \n",
      "Model Number: 124 of 146 with model FFT for Validation 2\n",
      "124 - FFT with avg smape 9.75: \n",
      "Model Number: 125 of 146 with model Cassandra for Validation 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125 - Cassandra with avg smape 14.94: \n",
      "Model Number: 126 of 146 with model UnivariateMotif for Validation 2\n",
      "126 - UnivariateMotif with avg smape 13.84: \n",
      "Model Number: 127 of 146 with model WindowRegression for Validation 2\n",
      "127 - WindowRegression with avg smape 10.55: \n",
      "Model Number: 128 of 146 with model FFT for Validation 2\n",
      "128 - FFT with avg smape 12.39: \n",
      "Model Number: 129 of 146 with model FFT for Validation 2\n",
      "129 - FFT with avg smape 12.39: \n",
      "Model Number: 130 of 146 with model UnobservedComponents for Validation 2\n",
      "130 - UnobservedComponents with avg smape 18.22: \n",
      "Model Number: 131 of 146 with model UnobservedComponents for Validation 2\n",
      "131 - UnobservedComponents with avg smape 15.46: \n",
      "Model Number: 132 of 146 with model MAR for Validation 2\n",
      "132 - MAR with avg smape 14.46: \n",
      "Model Number: 133 of 146 with model DatepartRegression for Validation 2\n",
      "133 - DatepartRegression with avg smape 13.58: \n",
      "Model Number: 134 of 146 with model Cassandra for Validation 2\n",
      "Template Eval Error: ValueError('operands could not be broadcast together with shapes (535,5) (541,5) ') in model 134 in generation 0: Cassandra\n",
      "Model Number: 135 of 146 with model MAR for Validation 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\tools\\percentile.py:47: RuntimeWarning: All-NaN slice encountered\n",
      "  max_val = np.nanmax(arr)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "135 - MAR with avg smape 14.1: \n",
      "Model Number: 136 of 146 with model SeasonalNaive for Validation 2\n",
      "136 - SeasonalNaive with avg smape 24.64: \n",
      "Model Number: 137 of 146 with model AverageValueNaive for Validation 2\n",
      "137 - AverageValueNaive with avg smape 10.7: \n",
      "Model Number: 138 of 146 with model Theta for Validation 2\n",
      "138 - Theta with avg smape 11.14: \n",
      "Model Number: 139 of 146 with model VAR for Validation 2\n",
      "139 - VAR with avg smape 20.53: \n",
      "Model Number: 140 of 146 with model FBProphet for Validation 2\n",
      "140 - FBProphet with avg smape 14.46: \n",
      "Model Number: 141 of 146 with model FBProphet for Validation 2\n",
      "141 - FBProphet with avg smape 14.46: \n",
      "Model Number: 142 of 146 with model FBProphet for Validation 2\n",
      "142 - FBProphet with avg smape 14.57: \n",
      "Model Number: 143 of 146 with model Theta for Validation 2\n",
      "143 - Theta with avg smape 26.65: \n",
      "Model Number: 144 of 146 with model MultivariateMotif for Validation 2\n",
      "144 - MultivariateMotif with avg smape 18.02: \n",
      "Model Number: 145 of 146 with model AverageValueNaive for Validation 2\n",
      "145 - AverageValueNaive with avg smape 14.9: \n",
      "Model Number: 146 of 146 with model FBProphet for Validation 2\n",
      "146 - FBProphet with avg smape 15.6: \n",
      "Validation Round: 3\n",
      "Model Number: 1 of 146 with model Ensemble for Validation 3\n",
      "📈 1 - Ensemble with avg smape 21.57: \n",
      "Model Number: 2 of 146 with model Ensemble for Validation 3\n",
      "2 - Ensemble with avg smape 22.16: \n",
      "Model Number: 3 of 146 with model Ensemble for Validation 3\n",
      "3 - Ensemble with avg smape 23.44: \n",
      "Model Number: 4 of 146 with model Ensemble for Validation 3\n",
      "4 - Ensemble with avg smape 22.53: \n",
      "Model Number: 5 of 146 with model ARIMA for Validation 3\n",
      "5 - ARIMA with avg smape 22.38: \n",
      "Model Number: 6 of 146 with model FBProphet for Validation 3\n",
      "6 - FBProphet with avg smape 22.11: \n",
      "Model Number: 7 of 146 with model Ensemble for Validation 3\n",
      "7 - Ensemble with avg smape 22.13: \n",
      "Model Number: 8 of 146 with model FBProphet for Validation 3\n",
      "8 - FBProphet with avg smape 22.11: \n",
      "Model Number: 9 of 146 with model FBProphet for Validation 3\n",
      "9 - FBProphet with avg smape 22.29: \n",
      "Model Number: 10 of 146 with model Ensemble for Validation 3\n",
      "📈 10 - Ensemble with avg smape 20.35: \n",
      "Model Number: 11 of 146 with model FBProphet for Validation 3\n",
      "📈 11 - FBProphet with avg smape 18.77: \n",
      "Model Number: 12 of 146 with model FBProphet for Validation 3\n",
      "12 - FBProphet with avg smape 22.48: \n",
      "Model Number: 13 of 146 with model FBProphet for Validation 3\n",
      "13 - FBProphet with avg smape 22.51: \n",
      "Model Number: 14 of 146 with model ARIMA for Validation 3\n",
      "14 - ARIMA with avg smape 22.28: \n",
      "Model Number: 15 of 146 with model ARIMA for Validation 3\n",
      "15 - ARIMA with avg smape 22.28: \n",
      "Model Number: 16 of 146 with model LastValueNaive for Validation 3\n",
      "16 - LastValueNaive with avg smape 26.1: \n",
      "Model Number: 17 of 146 with model LastValueNaive for Validation 3\n",
      "17 - LastValueNaive with avg smape 26.1: \n",
      "Model Number: 18 of 146 with model LastValueNaive for Validation 3\n",
      "18 - LastValueNaive with avg smape 26.16: \n",
      "Model Number: 19 of 146 with model LastValueNaive for Validation 3\n",
      "19 - LastValueNaive with avg smape 26.23: \n",
      "Model Number: 20 of 146 with model LastValueNaive for Validation 3\n",
      "📈 20 - LastValueNaive with avg smape 17.12: \n",
      "Model Number: 21 of 146 with model Theta for Validation 3\n",
      "21 - Theta with avg smape 18.46: \n",
      "Model Number: 22 of 146 with model LastValueNaive for Validation 3\n",
      "22 - LastValueNaive with avg smape 26.23: \n",
      "Model Number: 23 of 146 with model ARIMA for Validation 3\n",
      "23 - ARIMA with avg smape 20.87: \n",
      "Model Number: 24 of 146 with model Theta for Validation 3\n",
      "24 - Theta with avg smape 18.77: \n",
      "Model Number: 25 of 146 with model ARIMA for Validation 3\n",
      "25 - ARIMA with avg smape 28.45: \n",
      "Model Number: 26 of 146 with model VECM for Validation 3\n",
      "26 - VECM with avg smape 23.42: \n",
      "Model Number: 27 of 146 with model ETS for Validation 3\n",
      "27 - ETS with avg smape 30.26: \n",
      "Model Number: 28 of 146 with model ETS for Validation 3\n",
      "28 - ETS with avg smape 30.38: \n",
      "Model Number: 29 of 146 with model ETS for Validation 3\n",
      "29 - ETS with avg smape 29.81: \n",
      "Model Number: 30 of 146 with model Theta for Validation 3\n",
      "30 - Theta with avg smape 21.27: \n",
      "Model Number: 31 of 146 with model ETS for Validation 3\n",
      "31 - ETS with avg smape 26.98: \n",
      "Model Number: 32 of 146 with model ETS for Validation 3\n",
      "32 - ETS with avg smape 27.0: \n",
      "Model Number: 33 of 146 with model ETS for Validation 3\n",
      "33 - ETS with avg smape 26.99: \n",
      "Model Number: 34 of 146 with model ARIMA for Validation 3\n",
      "34 - ARIMA with avg smape 33.2: \n",
      "Model Number: 35 of 146 with model AverageValueNaive for Validation 3\n",
      "35 - AverageValueNaive with avg smape 21.47: \n",
      "Model Number: 36 of 146 with model Theta for Validation 3\n",
      "36 - Theta with avg smape 17.34: \n",
      "Model Number: 37 of 146 with model Theta for Validation 3\n",
      "📈 37 - Theta with avg smape 16.64: \n",
      "Model Number: 38 of 146 with model Theta for Validation 3\n",
      "38 - Theta with avg smape 16.85: \n",
      "Model Number: 39 of 146 with model MetricMotif for Validation 3\n",
      "39 - MetricMotif with avg smape 20.41: \n",
      "Model Number: 40 of 146 with model VECM for Validation 3\n",
      "40 - VECM with avg smape 36.1: \n",
      "Model Number: 41 of 146 with model SeasonalityMotif for Validation 3\n",
      "41 - SeasonalityMotif with avg smape 48.27: \n",
      "Model Number: 42 of 146 with model UnobservedComponents for Validation 3\n",
      "42 - UnobservedComponents with avg smape 18.28: \n",
      "Model Number: 43 of 146 with model SeasonalityMotif for Validation 3\n",
      "📈 43 - SeasonalityMotif with avg smape 14.12: \n",
      "Model Number: 44 of 146 with model FFT for Validation 3\n",
      "44 - FFT with avg smape 20.46: \n",
      "Model Number: 45 of 146 with model ARCH for Validation 3\n",
      "45 - ARCH with avg smape 21.47: \n",
      "Model Number: 46 of 146 with model ARCH for Validation 3\n",
      "46 - ARCH with avg smape 21.47: \n",
      "Model Number: 47 of 146 with model SeasonalityMotif for Validation 3\n",
      "47 - SeasonalityMotif with avg smape 38.46: \n",
      "Model Number: 48 of 146 with model SeasonalityMotif for Validation 3\n",
      "48 - SeasonalityMotif with avg smape 41.11: \n",
      "Model Number: 49 of 146 with model MetricMotif for Validation 3\n",
      "49 - MetricMotif with avg smape 19.77: \n",
      "Model Number: 50 of 146 with model MultivariateMotif for Validation 3\n",
      "50 - MultivariateMotif with avg smape 23.54: \n",
      "Model Number: 51 of 146 with model SeasonalityMotif for Validation 3\n",
      "51 - SeasonalityMotif with avg smape 28.44: \n",
      "Model Number: 52 of 146 with model ARDL for Validation 3\n",
      "52 - ARDL with avg smape 16.85: \n",
      "Model Number: 53 of 146 with model AverageValueNaive for Validation 3\n",
      "53 - AverageValueNaive with avg smape 15.34: \n",
      "Model Number: 54 of 146 with model AverageValueNaive for Validation 3\n",
      "54 - AverageValueNaive with avg smape 15.34: \n",
      "Model Number: 55 of 146 with model ARDL for Validation 3\n",
      "55 - ARDL with avg smape 16.85: \n",
      "Model Number: 56 of 146 with model MultivariateMotif for Validation 3\n",
      "56 - MultivariateMotif with avg smape 24.72: \n",
      "Model Number: 57 of 146 with model MetricMotif for Validation 3\n",
      "57 - MetricMotif with avg smape 19.57: \n",
      "Model Number: 58 of 146 with model KalmanStateSpace for Validation 3\n",
      "58 - KalmanStateSpace with avg smape 20.36: \n",
      "Model Number: 59 of 146 with model SectionalMotif for Validation 3\n",
      "59 - SectionalMotif with avg smape 14.61: \n",
      "Model Number: 60 of 146 with model VAR for Validation 3\n",
      "60 - VAR with avg smape 29.7: \n",
      "Model Number: 61 of 146 with model MetricMotif for Validation 3\n",
      "📈 61 - MetricMotif with avg smape 14.03: \n",
      "Model Number: 62 of 146 with model MetricMotif for Validation 3\n",
      "62 - MetricMotif with avg smape 17.41: \n",
      "Model Number: 63 of 146 with model VECM for Validation 3\n",
      "63 - VECM with avg smape 27.17: \n",
      "Model Number: 64 of 146 with model AverageValueNaive for Validation 3\n",
      "64 - AverageValueNaive with avg smape 17.01: \n",
      "Model Number: 65 of 146 with model GLS for Validation 3\n",
      "65 - GLS with avg smape 20.92: \n",
      "Model Number: 66 of 146 with model VECM for Validation 3\n",
      "66 - VECM with avg smape 20.58: \n",
      "Model Number: 67 of 146 with model Cassandra for Validation 3\n",
      "67 - Cassandra with avg smape 20.2: \n",
      "Model Number: 68 of 146 with model VAR for Validation 3\n",
      "68 - VAR with avg smape 27.26: \n",
      "Model Number: 69 of 146 with model MultivariateMotif for Validation 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69 - MultivariateMotif with avg smape 23.05: \n",
      "Model Number: 70 of 146 with model MultivariateMotif for Validation 3\n",
      "70 - MultivariateMotif with avg smape 24.51: \n",
      "Model Number: 71 of 146 with model SeasonalNaive for Validation 3\n",
      "71 - SeasonalNaive with avg smape 23.82: \n",
      "Model Number: 72 of 146 with model ARCH for Validation 3\n",
      "72 - ARCH with avg smape 17.05: \n",
      "Model Number: 73 of 146 with model VAR for Validation 3\n",
      "📈 73 - VAR with avg smape 12.85: \n",
      "Model Number: 74 of 146 with model VAR for Validation 3\n",
      "74 - VAR with avg smape 12.85: \n",
      "Model Number: 75 of 146 with model VAR for Validation 3\n",
      "75 - VAR with avg smape 12.85: \n",
      "Model Number: 76 of 146 with model VAR for Validation 3\n",
      "76 - VAR with avg smape 12.85: \n",
      "Model Number: 77 of 146 with model SeasonalNaive for Validation 3\n",
      "77 - SeasonalNaive with avg smape 24.31: \n",
      "Model Number: 78 of 146 with model VECM for Validation 3\n",
      "78 - VECM with avg smape 31.2: \n",
      "Model Number: 79 of 146 with model Cassandra for Validation 3\n",
      "79 - Cassandra with avg smape 21.67: \n",
      "Model Number: 80 of 146 with model WindowRegression for Validation 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80 - WindowRegression with avg smape 17.76: \n",
      "Model Number: 81 of 146 with model MultivariateMotif for Validation 3\n",
      "81 - MultivariateMotif with avg smape 22.86: \n",
      "Model Number: 82 of 146 with model ConstantNaive for Validation 3\n",
      "82 - ConstantNaive with avg smape 21.35: \n",
      "Model Number: 83 of 146 with model ARCH for Validation 3\n",
      "83 - ARCH with avg smape 16.99: \n",
      "Model Number: 84 of 146 with model AverageValueNaive for Validation 3\n",
      "84 - AverageValueNaive with avg smape 21.35: \n",
      "Model Number: 85 of 146 with model ConstantNaive for Validation 3\n",
      "85 - ConstantNaive with avg smape 21.35: \n",
      "Model Number: 86 of 146 with model DatepartRegression for Validation 3\n",
      "86 - DatepartRegression with avg smape 21.47: \n",
      "Model Number: 87 of 146 with model VECM for Validation 3\n",
      "87 - VECM with avg smape 21.47: \n",
      "Model Number: 88 of 146 with model UnivariateMotif for Validation 3\n",
      "88 - UnivariateMotif with avg smape 21.47: \n",
      "Model Number: 89 of 146 with model ConstantNaive for Validation 3\n",
      "89 - ConstantNaive with avg smape 21.47: \n",
      "Model Number: 90 of 146 with model ConstantNaive for Validation 3\n",
      "90 - ConstantNaive with avg smape 21.47: \n",
      "Model Number: 91 of 146 with model ConstantNaive for Validation 3\n",
      "91 - ConstantNaive with avg smape 21.47: \n",
      "Model Number: 92 of 146 with model SeasonalNaive for Validation 3\n",
      "92 - SeasonalNaive with avg smape 23.88: \n",
      "Model Number: 93 of 146 with model ARCH for Validation 3\n",
      "93 - ARCH with avg smape 187.25: \n",
      "Model Number: 94 of 146 with model MAR for Validation 3\n",
      "94 - MAR with avg smape 18.78: \n",
      "Model Number: 95 of 146 with model ARCH for Validation 3\n",
      "📈 95 - ARCH with avg smape 11.96: \n",
      "Model Number: 96 of 146 with model AverageValueNaive for Validation 3\n",
      "96 - AverageValueNaive with avg smape 21.47: \n",
      "Model Number: 97 of 146 with model UnobservedComponents for Validation 3\n",
      "97 - UnobservedComponents with avg smape 21.47: \n",
      "Model Number: 98 of 146 with model UnobservedComponents for Validation 3\n",
      "98 - UnobservedComponents with avg smape 21.47: \n",
      "Model Number: 99 of 146 with model SectionalMotif for Validation 3\n",
      "📈 99 - SectionalMotif with avg smape 11.66: \n",
      "Model Number: 100 of 146 with model FFT for Validation 3\n",
      "100 - FFT with avg smape 36.62: \n",
      "Model Number: 101 of 146 with model WindowRegression for Validation 3\n",
      "101 - WindowRegression with avg smape 16.38: \n",
      "Model Number: 102 of 146 with model ConstantNaive for Validation 3\n",
      "102 - ConstantNaive with avg smape 17.01: \n",
      "Model Number: 103 of 146 with model WindowRegression for Validation 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:2412: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 246293.44470277056, tolerance: 2830.6065043214276\n",
      "  ) = cd_fast.enet_coordinate_descent_multi_task(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103 - WindowRegression with avg smape 17.01: \n",
      "Model Number: 104 of 146 with model MultivariateMotif for Validation 3\n",
      "104 - MultivariateMotif with avg smape 17.01: \n",
      "Model Number: 105 of 146 with model SeasonalityMotif for Validation 3\n",
      "105 - SeasonalityMotif with avg smape 20.31: \n",
      "Model Number: 106 of 146 with model SectionalMotif for Validation 3\n",
      "106 - SectionalMotif with avg smape 16.2: \n",
      "Model Number: 107 of 146 with model GLS for Validation 3\n",
      "107 - GLS with avg smape 22.61: \n",
      "Model Number: 108 of 146 with model UnobservedComponents for Validation 3\n",
      "108 - UnobservedComponents with avg smape 17.01: \n",
      "Model Number: 109 of 146 with model FFT for Validation 3\n",
      "109 - FFT with avg smape 13.07: \n",
      "Model Number: 110 of 146 with model GLS for Validation 3\n",
      "110 - GLS with avg smape 26.23: \n",
      "Model Number: 111 of 146 with model SectionalMotif for Validation 3\n",
      "111 - SectionalMotif with avg smape 23.34: \n",
      "Model Number: 112 of 146 with model WindowRegression for Validation 3\n",
      "112 - WindowRegression with avg smape 19.69: \n",
      "Model Number: 113 of 146 with model RRVAR for Validation 3\n",
      "113 - RRVAR with avg smape 31.52: \n",
      "Model Number: 114 of 146 with model ARDL for Validation 3\n",
      "📈 114 - ARDL with avg smape 7.46: \n",
      "Model Number: 115 of 146 with model GLS for Validation 3\n",
      "115 - GLS with avg smape 21.16: \n",
      "Model Number: 116 of 146 with model GLS for Validation 3\n",
      "116 - GLS with avg smape 21.16: \n",
      "Model Number: 117 of 146 with model SeasonalNaive for Validation 3\n",
      "117 - SeasonalNaive with avg smape 20.37: \n",
      "Model Number: 118 of 146 with model WindowRegression for Validation 3\n",
      "118 - WindowRegression with avg smape 19.76: \n",
      "Model Number: 119 of 146 with model SectionalMotif for Validation 3\n",
      "119 - SectionalMotif with avg smape 18.87: \n",
      "Model Number: 120 of 146 with model DatepartRegression for Validation 3\n",
      "120 - DatepartRegression with avg smape 21.79: \n",
      "Model Number: 121 of 146 with model GLS for Validation 3\n",
      "121 - GLS with avg smape 21.28: \n",
      "Model Number: 122 of 146 with model MetricMotif for Validation 3\n",
      "122 - MetricMotif with avg smape 15.35: \n",
      "Model Number: 123 of 146 with model RRVAR for Validation 3\n",
      "123 - RRVAR with avg smape 20.78: \n",
      "Model Number: 124 of 146 with model FFT for Validation 3\n",
      "124 - FFT with avg smape 31.94: \n",
      "Model Number: 125 of 146 with model Cassandra for Validation 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125 - Cassandra with avg smape 21.34: \n",
      "Model Number: 126 of 146 with model UnivariateMotif for Validation 3\n",
      "Template Eval Error: ValueError('window shape cannot be larger than input array shape') in model 126 in generation 0: UnivariateMotif\n",
      "Model Number: 127 of 146 with model WindowRegression for Validation 3\n",
      "127 - WindowRegression with avg smape 17.58: \n",
      "Model Number: 128 of 146 with model FFT for Validation 3\n",
      "128 - FFT with avg smape 30.11: \n",
      "Model Number: 129 of 146 with model FFT for Validation 3\n",
      "129 - FFT with avg smape 30.11: \n",
      "Model Number: 130 of 146 with model UnobservedComponents for Validation 3\n",
      "130 - UnobservedComponents with avg smape 21.47: \n",
      "Model Number: 131 of 146 with model UnobservedComponents for Validation 3\n",
      "131 - UnobservedComponents with avg smape 21.47: \n",
      "Model Number: 132 of 146 with model MAR for Validation 3\n",
      "132 - MAR with avg smape 14.35: \n",
      "Model Number: 133 of 146 with model DatepartRegression for Validation 3\n",
      "133 - DatepartRegression with avg smape 18.95: \n",
      "Model Number: 134 of 146 with model Cassandra for Validation 3\n",
      "Template Eval Error: ValueError('operands could not be broadcast together with shapes (433,5) (490,5) ') in model 134 in generation 0: Cassandra\n",
      "Model Number: 135 of 146 with model MAR for Validation 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\tools\\percentile.py:47: RuntimeWarning: All-NaN slice encountered\n",
      "  max_val = np.nanmax(arr)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "135 - MAR with avg smape 46.0: \n",
      "Model Number: 136 of 146 with model SeasonalNaive for Validation 3\n",
      "136 - SeasonalNaive with avg smape 10.43: \n",
      "Model Number: 137 of 146 with model AverageValueNaive for Validation 3\n",
      "137 - AverageValueNaive with avg smape 15.34: \n",
      "Model Number: 138 of 146 with model Theta for Validation 3\n",
      "138 - Theta with avg smape 16.0: \n",
      "Model Number: 139 of 146 with model VAR for Validation 3\n",
      "139 - VAR with avg smape 23.75: \n",
      "Model Number: 140 of 146 with model FBProphet for Validation 3\n",
      "140 - FBProphet with avg smape 22.19: \n",
      "Model Number: 141 of 146 with model FBProphet for Validation 3\n",
      "141 - FBProphet with avg smape 22.19: \n",
      "Model Number: 142 of 146 with model FBProphet for Validation 3\n",
      "142 - FBProphet with avg smape 21.5: \n",
      "Model Number: 143 of 146 with model Theta for Validation 3\n",
      "143 - Theta with avg smape 20.71: \n",
      "Model Number: 144 of 146 with model MultivariateMotif for Validation 3\n",
      "144 - MultivariateMotif with avg smape 23.41: \n",
      "Model Number: 145 of 146 with model AverageValueNaive for Validation 3\n",
      "145 - AverageValueNaive with avg smape 15.34: \n",
      "Model Number: 146 of 146 with model FBProphet for Validation 3\n",
      "146 - FBProphet with avg smape 12.59: \n",
      "Validation Round: 4\n",
      "Model Number: 1 of 146 with model Ensemble for Validation 4\n",
      "📈 1 - Ensemble with avg smape 7.17: \n",
      "Model Number: 2 of 146 with model Ensemble for Validation 4\n",
      "📈 2 - Ensemble with avg smape 7.01: \n",
      "Model Number: 3 of 146 with model Ensemble for Validation 4\n",
      "📈 3 - Ensemble with avg smape 5.97: \n",
      "Model Number: 4 of 146 with model Ensemble for Validation 4\n",
      "4 - Ensemble with avg smape 6.6: \n",
      "Model Number: 5 of 146 with model ARIMA for Validation 4\n",
      "5 - ARIMA with avg smape 8.56: \n",
      "Model Number: 6 of 146 with model FBProphet for Validation 4\n",
      "6 - FBProphet with avg smape 6.84: \n",
      "Model Number: 7 of 146 with model Ensemble for Validation 4\n",
      "7 - Ensemble with avg smape 6.84: \n",
      "Model Number: 8 of 146 with model FBProphet for Validation 4\n",
      "8 - FBProphet with avg smape 6.84: \n",
      "Model Number: 9 of 146 with model FBProphet for Validation 4\n",
      "9 - FBProphet with avg smape 6.85: \n",
      "Model Number: 10 of 146 with model Ensemble for Validation 4\n",
      "10 - Ensemble with avg smape 6.44: \n",
      "Model Number: 11 of 146 with model FBProphet for Validation 4\n",
      "11 - FBProphet with avg smape 6.51: \n",
      "Model Number: 12 of 146 with model FBProphet for Validation 4\n",
      "12 - FBProphet with avg smape 6.8: \n",
      "Model Number: 13 of 146 with model FBProphet for Validation 4\n",
      "13 - FBProphet with avg smape 6.88: \n",
      "Model Number: 14 of 146 with model ARIMA for Validation 4\n",
      "14 - ARIMA with avg smape 6.07: \n",
      "Model Number: 15 of 146 with model ARIMA for Validation 4\n",
      "15 - ARIMA with avg smape 6.07: \n",
      "Model Number: 16 of 146 with model LastValueNaive for Validation 4\n",
      "16 - LastValueNaive with avg smape 6.21: \n",
      "Model Number: 17 of 146 with model LastValueNaive for Validation 4\n",
      "17 - LastValueNaive with avg smape 6.21: \n",
      "Model Number: 18 of 146 with model LastValueNaive for Validation 4\n",
      "18 - LastValueNaive with avg smape 6.32: \n",
      "Model Number: 19 of 146 with model LastValueNaive for Validation 4\n",
      "📈 19 - LastValueNaive with avg smape 5.43: \n",
      "Model Number: 20 of 146 with model LastValueNaive for Validation 4\n",
      "20 - LastValueNaive with avg smape 6.64: \n",
      "Model Number: 21 of 146 with model Theta for Validation 4\n",
      "21 - Theta with avg smape 6.23: \n",
      "Model Number: 22 of 146 with model LastValueNaive for Validation 4\n",
      "22 - LastValueNaive with avg smape 5.43: \n",
      "Model Number: 23 of 146 with model ARIMA for Validation 4\n",
      "23 - ARIMA with avg smape 6.64: \n",
      "Model Number: 24 of 146 with model Theta for Validation 4\n",
      "24 - Theta with avg smape 6.76: \n",
      "Model Number: 25 of 146 with model ARIMA for Validation 4\n",
      "25 - ARIMA with avg smape 8.61: \n",
      "Model Number: 26 of 146 with model VECM for Validation 4\n",
      "26 - VECM with avg smape 8.06: \n",
      "Model Number: 27 of 146 with model ETS for Validation 4\n",
      "27 - ETS with avg smape 5.86: \n",
      "Model Number: 28 of 146 with model ETS for Validation 4\n",
      "28 - ETS with avg smape 5.84: \n",
      "Model Number: 29 of 146 with model ETS for Validation 4\n",
      "29 - ETS with avg smape 5.93: \n",
      "Model Number: 30 of 146 with model Theta for Validation 4\n",
      "30 - Theta with avg smape 6.62: \n",
      "Model Number: 31 of 146 with model ETS for Validation 4\n",
      "31 - ETS with avg smape 5.86: \n",
      "Model Number: 32 of 146 with model ETS for Validation 4\n",
      "32 - ETS with avg smape 6.65: \n",
      "Model Number: 33 of 146 with model ETS for Validation 4\n",
      "33 - ETS with avg smape 5.58: \n",
      "Model Number: 34 of 146 with model ARIMA for Validation 4\n",
      "34 - ARIMA with avg smape 44.02: \n",
      "Model Number: 35 of 146 with model AverageValueNaive for Validation 4\n",
      "35 - AverageValueNaive with avg smape 6.3: \n",
      "Model Number: 36 of 146 with model Theta for Validation 4\n",
      "36 - Theta with avg smape 6.66: \n",
      "Model Number: 37 of 146 with model Theta for Validation 4\n",
      "37 - Theta with avg smape 6.06: \n",
      "Model Number: 38 of 146 with model Theta for Validation 4\n",
      "38 - Theta with avg smape 6.46: \n",
      "Model Number: 39 of 146 with model MetricMotif for Validation 4\n",
      "39 - MetricMotif with avg smape 7.62: \n",
      "Model Number: 40 of 146 with model VECM for Validation 4\n",
      "40 - VECM with avg smape 5.97: \n",
      "Model Number: 41 of 146 with model SeasonalityMotif for Validation 4\n",
      "41 - SeasonalityMotif with avg smape 10.17: \n",
      "Model Number: 42 of 146 with model UnobservedComponents for Validation 4\n",
      "42 - UnobservedComponents with avg smape 7.89: \n",
      "Model Number: 43 of 146 with model SeasonalityMotif for Validation 4\n",
      "43 - SeasonalityMotif with avg smape 8.28: \n",
      "Model Number: 44 of 146 with model FFT for Validation 4\n",
      "44 - FFT with avg smape 7.68: \n",
      "Model Number: 45 of 146 with model ARCH for Validation 4\n",
      "45 - ARCH with avg smape 6.3: \n",
      "Model Number: 46 of 146 with model ARCH for Validation 4\n",
      "46 - ARCH with avg smape 6.3: \n",
      "Model Number: 47 of 146 with model SeasonalityMotif for Validation 4\n",
      "47 - SeasonalityMotif with avg smape 11.76: \n",
      "Model Number: 48 of 146 with model SeasonalityMotif for Validation 4\n",
      "48 - SeasonalityMotif with avg smape 9.03: \n",
      "Model Number: 49 of 146 with model MetricMotif for Validation 4\n",
      "📈 49 - MetricMotif with avg smape 5.25: \n",
      "Model Number: 50 of 146 with model MultivariateMotif for Validation 4\n",
      "50 - MultivariateMotif with avg smape 7.57: \n",
      "Model Number: 51 of 146 with model SeasonalityMotif for Validation 4\n",
      "51 - SeasonalityMotif with avg smape 10.32: \n",
      "Model Number: 52 of 146 with model ARDL for Validation 4\n",
      "52 - ARDL with avg smape 7.52: \n",
      "Model Number: 53 of 146 with model AverageValueNaive for Validation 4\n",
      "53 - AverageValueNaive with avg smape 6.86: \n",
      "Model Number: 54 of 146 with model AverageValueNaive for Validation 4\n",
      "54 - AverageValueNaive with avg smape 6.86: \n",
      "Model Number: 55 of 146 with model ARDL for Validation 4\n",
      "55 - ARDL with avg smape 7.52: \n",
      "Model Number: 56 of 146 with model MultivariateMotif for Validation 4\n",
      "56 - MultivariateMotif with avg smape 8.37: \n",
      "Model Number: 57 of 146 with model MetricMotif for Validation 4\n",
      "57 - MetricMotif with avg smape 5.6: \n",
      "Model Number: 58 of 146 with model KalmanStateSpace for Validation 4\n",
      "58 - KalmanStateSpace with avg smape 6.74: \n",
      "Model Number: 59 of 146 with model SectionalMotif for Validation 4\n",
      "59 - SectionalMotif with avg smape 6.54: \n",
      "Model Number: 60 of 146 with model VAR for Validation 4\n",
      "Template Eval Error: ValueError('maxlags is too large for the number of observations and the number of equations. The largest model cannot be estimated.') in model 60 in generation 0: VAR\n",
      "Model Number: 61 of 146 with model MetricMotif for Validation 4\n",
      "61 - MetricMotif with avg smape 6.78: \n",
      "Model Number: 62 of 146 with model MetricMotif for Validation 4\n",
      "62 - MetricMotif with avg smape 6.39: \n",
      "Model Number: 63 of 146 with model VECM for Validation 4\n",
      "63 - VECM with avg smape 8.75: \n",
      "Model Number: 64 of 146 with model AverageValueNaive for Validation 4\n",
      "64 - AverageValueNaive with avg smape 5.95: \n",
      "Model Number: 65 of 146 with model GLS for Validation 4\n",
      "65 - GLS with avg smape 6.27: \n",
      "Model Number: 66 of 146 with model VECM for Validation 4\n",
      "66 - VECM with avg smape 7.28: \n",
      "Model Number: 67 of 146 with model Cassandra for Validation 4\n",
      "67 - Cassandra with avg smape 9.26: \n",
      "Model Number: 68 of 146 with model VAR for Validation 4\n",
      "Template Eval Error: ValueError('maxlags is too large for the number of observations and the number of equations. The largest model cannot be estimated.') in model 68 in generation 0: VAR\n",
      "Model Number: 69 of 146 with model MultivariateMotif for Validation 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69 - MultivariateMotif with avg smape 8.18: \n",
      "Model Number: 70 of 146 with model MultivariateMotif for Validation 4\n",
      "70 - MultivariateMotif with avg smape 8.72: \n",
      "Model Number: 71 of 146 with model SeasonalNaive for Validation 4\n",
      "71 - SeasonalNaive with avg smape 7.46: \n",
      "Model Number: 72 of 146 with model ARCH for Validation 4\n",
      "72 - ARCH with avg smape 7.57: \n",
      "Model Number: 73 of 146 with model VAR for Validation 4\n",
      "Template Eval Error: ValueError('maxlags is too large for the number of observations and the number of equations. The largest model cannot be estimated.') in model 73 in generation 0: VAR\n",
      "Model Number: 74 of 146 with model VAR for Validation 4\n",
      "Template Eval Error: ValueError('maxlags is too large for the number of observations and the number of equations. The largest model cannot be estimated.') in model 74 in generation 0: VAR\n",
      "Model Number: 75 of 146 with model VAR for Validation 4\n",
      "Template Eval Error: ValueError('maxlags is too large for the number of observations and the number of equations. The largest model cannot be estimated.') in model 75 in generation 0: VAR\n",
      "Model Number: 76 of 146 with model VAR for Validation 4\n",
      "76 - VAR with avg smape 7.0: \n",
      "Model Number: 77 of 146 with model SeasonalNaive for Validation 4\n",
      "77 - SeasonalNaive with avg smape 8.14: \n",
      "Model Number: 78 of 146 with model VECM for Validation 4\n",
      "78 - VECM with avg smape 5.71: \n",
      "Model Number: 79 of 146 with model Cassandra for Validation 4\n",
      "📈 79 - Cassandra with avg smape 5.22: \n",
      "Model Number: 80 of 146 with model WindowRegression for Validation 4\n",
      "80 - WindowRegression with avg smape 9.67: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Number: 81 of 146 with model MultivariateMotif for Validation 4\n",
      "81 - MultivariateMotif with avg smape 7.95: \n",
      "Model Number: 82 of 146 with model ConstantNaive for Validation 4\n",
      "82 - ConstantNaive with avg smape 6.26: \n",
      "Model Number: 83 of 146 with model ARCH for Validation 4\n",
      "83 - ARCH with avg smape 9.09: \n",
      "Model Number: 84 of 146 with model AverageValueNaive for Validation 4\n",
      "Template Eval Error: Exception('Transformer RegressionFilter failed on fit') in model 84 in generation 0: AverageValueNaive\n",
      "Model Number: 85 of 146 with model ConstantNaive for Validation 4\n",
      "85 - ConstantNaive with avg smape 6.26: \n",
      "Model Number: 86 of 146 with model DatepartRegression for Validation 4\n",
      "86 - DatepartRegression with avg smape 6.3: \n",
      "Model Number: 87 of 146 with model VECM for Validation 4\n",
      "87 - VECM with avg smape 6.3: \n",
      "Model Number: 88 of 146 with model UnivariateMotif for Validation 4\n",
      "88 - UnivariateMotif with avg smape 6.3: \n",
      "Model Number: 89 of 146 with model ConstantNaive for Validation 4\n",
      "89 - ConstantNaive with avg smape 6.3: \n",
      "Model Number: 90 of 146 with model ConstantNaive for Validation 4\n",
      "90 - ConstantNaive with avg smape 6.3: \n",
      "Model Number: 91 of 146 with model ConstantNaive for Validation 4\n",
      "91 - ConstantNaive with avg smape 6.3: \n",
      "Model Number: 92 of 146 with model SeasonalNaive for Validation 4\n",
      "92 - SeasonalNaive with avg smape 7.38: \n",
      "Model Number: 93 of 146 with model ARCH for Validation 4\n",
      "93 - ARCH with avg smape 8.2: \n",
      "Model Number: 94 of 146 with model MAR for Validation 4\n",
      "Template Eval Error: ValueError(\"Model returned NaN due to a preprocessing transformer {'fillna': 'mean', 'transformations': {'0': 'MinMaxScaler', '1': 'SinTrend', '2': 'PowerTransformer'}, 'transformation_params': {'0': {}, '1': {}, '2': {}}}. fail_on_forecast_nan=True\") in model 94 in generation 0: MAR\n",
      "Model Number: 95 of 146 with model ARCH for Validation 4\n",
      "95 - ARCH with avg smape 7.92: \n",
      "Model Number: 96 of 146 with model AverageValueNaive for Validation 4\n",
      "96 - AverageValueNaive with avg smape 6.3: \n",
      "Model Number: 97 of 146 with model UnobservedComponents for Validation 4\n",
      "97 - UnobservedComponents with avg smape 6.3: \n",
      "Model Number: 98 of 146 with model UnobservedComponents for Validation 4\n",
      "98 - UnobservedComponents with avg smape 6.3: \n",
      "Model Number: 99 of 146 with model SectionalMotif for Validation 4\n",
      "99 - SectionalMotif with avg smape 6.84: \n",
      "Model Number: 100 of 146 with model FFT for Validation 4\n",
      "100 - FFT with avg smape 5.79: \n",
      "Model Number: 101 of 146 with model WindowRegression for Validation 4\n",
      "Template Eval Error: Exception('Transformer STLFilter failed on fit') in model 101 in generation 0: WindowRegression\n",
      "Model Number: 102 of 146 with model ConstantNaive for Validation 4\n",
      "102 - ConstantNaive with avg smape 5.95: \n",
      "Model Number: 103 of 146 with model WindowRegression for Validation 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:2412: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 26240.83985117264, tolerance: 764.0008425880096\n",
      "  ) = cd_fast.enet_coordinate_descent_multi_task(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103 - WindowRegression with avg smape 5.95: \n",
      "Model Number: 104 of 146 with model MultivariateMotif for Validation 4\n",
      "104 - MultivariateMotif with avg smape 5.95: \n",
      "Model Number: 105 of 146 with model SeasonalityMotif for Validation 4\n",
      "105 - SeasonalityMotif with avg smape 7.75: \n",
      "Model Number: 106 of 146 with model SectionalMotif for Validation 4\n",
      "106 - SectionalMotif with avg smape 5.5: \n",
      "Model Number: 107 of 146 with model GLS for Validation 4\n",
      "107 - GLS with avg smape 8.05: \n",
      "Model Number: 108 of 146 with model UnobservedComponents for Validation 4\n",
      "108 - UnobservedComponents with avg smape 5.95: \n",
      "Model Number: 109 of 146 with model FFT for Validation 4\n",
      "109 - FFT with avg smape 6.24: \n",
      "Model Number: 110 of 146 with model GLS for Validation 4\n",
      "110 - GLS with avg smape 9.42: \n",
      "Model Number: 111 of 146 with model SectionalMotif for Validation 4\n",
      "Template Eval Error: ValueError('kth(=10) out of bounds (9)') in model 111 in generation 0: SectionalMotif\n",
      "Model Number: 112 of 146 with model WindowRegression for Validation 4\n",
      "Template Eval Error: Exception('Transformer STLFilter failed on fit') in model 112 in generation 0: WindowRegression\n",
      "Model Number: 113 of 146 with model RRVAR for Validation 4\n",
      "113 - RRVAR with avg smape 6.82: \n",
      "Model Number: 114 of 146 with model ARDL for Validation 4\n",
      "114 - ARDL with avg smape 7.8: \n",
      "Model Number: 115 of 146 with model GLS for Validation 4\n",
      "115 - GLS with avg smape 6.36: \n",
      "Model Number: 116 of 146 with model GLS for Validation 4\n",
      "116 - GLS with avg smape 6.36: \n",
      "Model Number: 117 of 146 with model SeasonalNaive for Validation 4\n",
      "117 - SeasonalNaive with avg smape 6.12: \n",
      "Model Number: 118 of 146 with model WindowRegression for Validation 4\n",
      "Template Eval Error: Exception('Transformer STLFilter failed on fit') in model 118 in generation 0: WindowRegression\n",
      "Model Number: 119 of 146 with model SectionalMotif for Validation 4\n",
      "119 - SectionalMotif with avg smape 7.45: \n",
      "Model Number: 120 of 146 with model DatepartRegression for Validation 4\n",
      "120 - DatepartRegression with avg smape 6.38: \n",
      "Model Number: 121 of 146 with model GLS for Validation 4\n",
      "121 - GLS with avg smape 6.3: \n",
      "Model Number: 122 of 146 with model MetricMotif for Validation 4\n",
      "122 - MetricMotif with avg smape 7.76: \n",
      "Model Number: 123 of 146 with model RRVAR for Validation 4\n",
      "123 - RRVAR with avg smape 6.73: \n",
      "Model Number: 124 of 146 with model FFT for Validation 4\n",
      "124 - FFT with avg smape 8.55: \n",
      "Model Number: 125 of 146 with model Cassandra for Validation 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125 - Cassandra with avg smape 5.44: \n",
      "Model Number: 126 of 146 with model UnivariateMotif for Validation 4\n",
      "Template Eval Error: ValueError('window shape cannot be larger than input array shape') in model 126 in generation 0: UnivariateMotif\n",
      "Model Number: 127 of 146 with model WindowRegression for Validation 4\n",
      "Template Eval Error: Exception('Transformer STLFilter failed on fit') in model 127 in generation 0: WindowRegression\n",
      "Model Number: 128 of 146 with model FFT for Validation 4\n",
      "128 - FFT with avg smape 8.77: \n",
      "Model Number: 129 of 146 with model FFT for Validation 4\n",
      "129 - FFT with avg smape 8.77: \n",
      "Model Number: 130 of 146 with model UnobservedComponents for Validation 4\n",
      "130 - UnobservedComponents with avg smape 6.3: \n",
      "Model Number: 131 of 146 with model UnobservedComponents for Validation 4\n",
      "131 - UnobservedComponents with avg smape 6.3: \n",
      "Model Number: 132 of 146 with model MAR for Validation 4\n",
      "Template Eval Error: Exception('Transformer DifferencedTransformer failed on inverse') in model 132 in generation 0: MAR\n",
      "Model Number: 133 of 146 with model DatepartRegression for Validation 4\n",
      "133 - DatepartRegression with avg smape 6.72: \n",
      "Model Number: 134 of 146 with model Cassandra for Validation 4\n",
      "Template Eval Error: ValueError('window shape cannot be larger than input array shape') in model 134 in generation 0: Cassandra\n",
      "Model Number: 135 of 146 with model MAR for Validation 4\n",
      "135 - MAR with avg smape 10.17: \n",
      "Model Number: 136 of 146 with model SeasonalNaive for Validation 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\numpy\\lib\\function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\tools\\percentile.py:47: RuntimeWarning: All-NaN slice encountered\n",
      "  max_val = np.nanmax(arr)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "136 - SeasonalNaive with avg smape 9.18: \n",
      "Model Number: 137 of 146 with model AverageValueNaive for Validation 4\n",
      "137 - AverageValueNaive with avg smape 6.86: \n",
      "Model Number: 138 of 146 with model Theta for Validation 4\n",
      "138 - Theta with avg smape 6.26: \n",
      "Model Number: 139 of 146 with model VAR for Validation 4\n",
      "139 - VAR with avg smape 18.3: \n",
      "Model Number: 140 of 146 with model FBProphet for Validation 4\n",
      "140 - FBProphet with avg smape 6.81: \n",
      "Model Number: 141 of 146 with model FBProphet for Validation 4\n",
      "141 - FBProphet with avg smape 6.81: \n",
      "Model Number: 142 of 146 with model FBProphet for Validation 4\n",
      "142 - FBProphet with avg smape 6.96: \n",
      "Model Number: 143 of 146 with model Theta for Validation 4\n",
      "143 - Theta with avg smape 5.6: \n",
      "Model Number: 144 of 146 with model MultivariateMotif for Validation 4\n",
      "144 - MultivariateMotif with avg smape 7.63: \n",
      "Model Number: 145 of 146 with model AverageValueNaive for Validation 4\n",
      "145 - AverageValueNaive with avg smape 10.27: \n",
      "Model Number: 146 of 146 with model FBProphet for Validation 4\n",
      "146 - FBProphet with avg smape 6.96: \n",
      "Model Number: 1491 with model Ensemble in generation 7 of Ensembles\n",
      "Model Number: 1492 with model Ensemble in generation 7 of Ensembles\n",
      "Model Number: 1493 with model Ensemble in generation 7 of Ensembles\n",
      "Model Number: 1494 with model Ensemble in generation 7 of Ensembles\n",
      "Model Number: 1495 with model Ensemble in generation 7 of Ensembles\n",
      "Model Number: 1496 with model Ensemble in generation 7 of Ensembles\n",
      "Model Number: 1497 with model Ensemble in generation 7 of Ensembles\n",
      "Model Number: 1498 with model Ensemble in generation 7 of Ensembles\n",
      "Model Number: 1499 with model Ensemble in generation 7 of Ensembles\n",
      "Model Number: 1500 with model Ensemble in generation 7 of Ensembles\n",
      "Model Number: 1501 with model Ensemble in generation 7 of Ensembles\n",
      "Model Number: 1502 with model Ensemble in generation 7 of Ensembles\n",
      "Validation Round: 1\n",
      "Model Number: 1 of 12 with model Ensemble for Validation 1\n",
      "📈 1 - Ensemble with avg smape 9.02: \n",
      "Model Number: 2 of 12 with model Ensemble for Validation 1\n",
      "2 - Ensemble with avg smape 9.54: \n",
      "Model Number: 3 of 12 with model Ensemble for Validation 1\n",
      "3 - Ensemble with avg smape 9.38: \n",
      "Model Number: 4 of 12 with model Ensemble for Validation 1\n",
      "4 - Ensemble with avg smape 9.43: \n",
      "Model Number: 5 of 12 with model Ensemble for Validation 1\n",
      "📈 5 - Ensemble with avg smape 8.97: \n",
      "Model Number: 6 of 12 with model Ensemble for Validation 1\n",
      "6 - Ensemble with avg smape 9.12: \n",
      "Model Number: 7 of 12 with model Ensemble for Validation 1\n",
      "7 - Ensemble with avg smape 9.69: \n",
      "Model Number: 8 of 12 with model Ensemble for Validation 1\n",
      "8 - Ensemble with avg smape 9.69: \n",
      "Model Number: 9 of 12 with model Ensemble for Validation 1\n",
      "📈 9 - Ensemble with avg smape 8.87: \n",
      "Model Number: 10 of 12 with model Ensemble for Validation 1\n",
      "10 - Ensemble with avg smape 9.7: \n",
      "Model Number: 11 of 12 with model Ensemble for Validation 1\n",
      "📈 11 - Ensemble with avg smape 8.75: \n",
      "Model Number: 12 of 12 with model Ensemble for Validation 1\n",
      "12 - Ensemble with avg smape 8.88: \n",
      "Validation Round: 2\n",
      "Model Number: 1 of 12 with model Ensemble for Validation 2\n",
      "📈 1 - Ensemble with avg smape 10.39: \n",
      "Model Number: 2 of 12 with model Ensemble for Validation 2\n",
      "📈 2 - Ensemble with avg smape 9.8: \n",
      "Model Number: 3 of 12 with model Ensemble for Validation 2\n",
      "📈 3 - Ensemble with avg smape 9.64: \n",
      "Model Number: 4 of 12 with model Ensemble for Validation 2\n",
      "📈 4 - Ensemble with avg smape 9.38: \n",
      "Model Number: 5 of 12 with model Ensemble for Validation 2\n",
      "5 - Ensemble with avg smape 11.48: \n",
      "Model Number: 6 of 12 with model Ensemble for Validation 2\n",
      "6 - Ensemble with avg smape 10.74: \n",
      "Model Number: 7 of 12 with model Ensemble for Validation 2\n",
      "7 - Ensemble with avg smape 11.76: \n",
      "Model Number: 8 of 12 with model Ensemble for Validation 2\n",
      "8 - Ensemble with avg smape 11.76: \n",
      "Model Number: 9 of 12 with model Ensemble for Validation 2\n",
      "9 - Ensemble with avg smape 10.92: \n",
      "Model Number: 10 of 12 with model Ensemble for Validation 2\n",
      "10 - Ensemble with avg smape 10.97: \n",
      "Model Number: 11 of 12 with model Ensemble for Validation 2\n",
      "11 - Ensemble with avg smape 11.38: \n",
      "Model Number: 12 of 12 with model Ensemble for Validation 2\n",
      "12 - Ensemble with avg smape 10.89: \n",
      "Validation Round: 3\n",
      "Model Number: 1 of 12 with model Ensemble for Validation 3\n",
      "📈 1 - Ensemble with avg smape 11.81: \n",
      "Model Number: 2 of 12 with model Ensemble for Validation 3\n",
      "2 - Ensemble with avg smape 16.18: \n",
      "Model Number: 3 of 12 with model Ensemble for Validation 3\n",
      "3 - Ensemble with avg smape 13.12: \n",
      "Model Number: 4 of 12 with model Ensemble for Validation 3\n",
      "4 - Ensemble with avg smape 14.92: \n",
      "Model Number: 5 of 12 with model Ensemble for Validation 3\n",
      "📈 5 - Ensemble with avg smape 11.78: \n",
      "Model Number: 6 of 12 with model Ensemble for Validation 3\n",
      "6 - Ensemble with avg smape 13.42: \n",
      "Model Number: 7 of 12 with model Ensemble for Validation 3\n",
      "7 - Ensemble with avg smape 20.52: \n",
      "Model Number: 8 of 12 with model Ensemble for Validation 3\n",
      "8 - Ensemble with avg smape 20.52: \n",
      "Model Number: 9 of 12 with model Ensemble for Validation 3\n",
      "📈 9 - Ensemble with avg smape 11.62: \n",
      "Model Number: 10 of 12 with model Ensemble for Validation 3\n",
      "10 - Ensemble with avg smape 16.7: \n",
      "Model Number: 11 of 12 with model Ensemble for Validation 3\n",
      "11 - Ensemble with avg smape 17.67: \n",
      "Model Number: 12 of 12 with model Ensemble for Validation 3\n",
      "📈 12 - Ensemble with avg smape 11.43: \n",
      "Validation Round: 4\n",
      "Model Number: 1 of 12 with model Ensemble for Validation 4\n",
      "📈 1 - Ensemble with avg smape 7.32: \n",
      "Model Number: 2 of 12 with model Ensemble for Validation 4\n",
      "2 - Ensemble with avg smape 7.56: \n",
      "Model Number: 3 of 12 with model Ensemble for Validation 4\n",
      "3 - Ensemble with avg smape 7.52: \n",
      "Model Number: 4 of 12 with model Ensemble for Validation 4\n",
      "4 - Ensemble with avg smape 7.56: \n",
      "Model Number: 5 of 12 with model Ensemble for Validation 4\n",
      "📈 5 - Ensemble with avg smape 6.85: \n",
      "Model Number: 6 of 12 with model Ensemble for Validation 4\n",
      "📈 6 - Ensemble with avg smape 6.58: \n",
      "Model Number: 7 of 12 with model Ensemble for Validation 4\n",
      "📈 7 - Ensemble with avg smape 6.57: \n",
      "Model Number: 8 of 12 with model Ensemble for Validation 4\n",
      "8 - Ensemble with avg smape 6.57: \n",
      "Model Number: 9 of 12 with model Ensemble for Validation 4\n",
      "9 - Ensemble with avg smape 6.65: \n",
      "Model Number: 10 of 12 with model Ensemble for Validation 4\n",
      "10 - Ensemble with avg smape 6.76: \n",
      "Model Number: 11 of 12 with model Ensemble for Validation 4\n",
      "11 - Ensemble with avg smape 7.01: \n",
      "Model Number: 12 of 12 with model Ensemble for Validation 4\n",
      "12 - Ensemble with avg smape 6.67: \n",
      "Model Number: 1 with model Ensemble in generation 0 of Horizontal Ensembles\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\ensemble.py:103: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  forecast_array = np.array(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1285, in model_forecast\n",
      "    df_forecast = model_forecast(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1330, in model_forecast\n",
      "    ens_forecast = EnsembleForecast(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\ensemble.py\", line 615, in EnsembleForecast\n",
      "    ens_forecast = BestNEnsemble(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\ensemble.py\", line 103, in BestNEnsemble\n",
      "    forecast_array = np.array(\n",
      "ValueError: could not broadcast input array from shape (15,5) into shape (15,)\n",
      "\n",
      "FAILED: Ensemble Horizontal component 5 of 5 Ensemble with error: ValueError('could not broadcast input array from shape (15,5) into shape (15,)')\n",
      "Model Number: 2 with model Ensemble in generation 0 of Horizontal Ensembles\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1285, in model_forecast\n",
      "    df_forecast = model_forecast(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1330, in model_forecast\n",
      "    ens_forecast = EnsembleForecast(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\ensemble.py\", line 615, in EnsembleForecast\n",
      "    ens_forecast = BestNEnsemble(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\ensemble.py\", line 103, in BestNEnsemble\n",
      "    forecast_array = np.array(\n",
      "ValueError: could not broadcast input array from shape (15,5) into shape (15,)\n",
      "\n",
      "FAILED: Ensemble Horizontal component 5 of 5 Ensemble with error: ValueError('could not broadcast input array from shape (15,5) into shape (15,)')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\ensemble.py:103: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  forecast_array = np.array(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1285, in model_forecast\n",
      "    df_forecast = model_forecast(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1330, in model_forecast\n",
      "    ens_forecast = EnsembleForecast(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\ensemble.py\", line 615, in EnsembleForecast\n",
      "    ens_forecast = BestNEnsemble(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\ensemble.py\", line 103, in BestNEnsemble\n",
      "    forecast_array = np.array(\n",
      "ValueError: could not broadcast input array from shape (15,1) into shape (15,)\n",
      "\n",
      "FAILED: Ensemble Horizontal component 6 of 5 Ensemble with error: ValueError('could not broadcast input array from shape (15,1) into shape (15,)')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\ensemble.py:103: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  forecast_array = np.array(\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\naive_bayes.py:510: RuntimeWarning: divide by zero encountered in log\n",
      "  n_ij = -0.5 * np.sum(np.log(2.0 * np.pi * self.var_[i, :]))\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\naive_bayes.py:511: RuntimeWarning: divide by zero encountered in divide\n",
      "  n_ij -= 0.5 * np.sum(((X - self.theta_[i, :]) ** 2) / (self.var_[i, :]), 1)\n",
      "C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\sklearn\\naive_bayes.py:511: RuntimeWarning: invalid value encountered in divide\n",
      "  n_ij -= 0.5 * np.sum(((X - self.theta_[i, :]) ** 2) / (self.var_[i, :]), 1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ValueError('zero-size array to reduction operation minimum which has no identity')\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [2018-01-05 00:00:00, 2018-01-12 00:00:00, 2018-01-19 00:00:00, 2018-01-26 00:00:00, 2018-02-02 00:00:00, 2018-02-09 00:00:00, 2018-02-16 00:00:00, 2018-02-23 00:00:00, 2018-03-02 00:00:00, 2018-03-09 00:00:00, 2018-03-16 00:00:00, 2018-03-23 00:00:00, 2018-03-30 00:00:00, 2018-04-06 00:00:00, 2018-04-13 00:00:00, 2018-04-20 00:00:00, 2018-04-27 00:00:00, 2018-05-04 00:00:00, 2018-05-11 00:00:00, 2018-05-18 00:00:00, 2018-05-25 00:00:00, 2018-06-01 00:00:00, 2018-06-08 00:00:00, 2018-06-15 00:00:00, 2018-06-22 00:00:00, 2018-06-29 00:00:00, 2018-07-06 00:00:00, 2018-07-13 00:00:00, 2018-07-20 00:00:00, 2018-07-27 00:00:00, 2018-08-03 00:00:00, 2018-08-10 00:00:00, 2018-08-17 00:00:00, 2018-08-24 00:00:00, 2018-08-31 00:00:00, 2018-09-07 00:00:00, 2018-09-14 00:00:00, 2018-09-21 00:00:00, 2018-09-28 00:00:00, 2018-10-05 00:00:00, 2018-10-12 00:00:00, 2018-10-19 00:00:00, 2018-10-26 00:00:00, 2018-11-02 00:00:00, 2018-11-09 00:00:00, 2018-11-16 00:00:00, 2018-11-23 00:00:00, 2018-11-30 00:00:00, 2018-12-07 00:00:00, 2018-12-14 00:00:00, 2018-12-21 00:00:00, 2018-12-28 00:00:00, 2019-01-04 00:00:00, 2019-01-11 00:00:00, 2019-01-18 00:00:00, 2019-01-25 00:00:00, 2019-02-01 00:00:00, 2019-02-08 00:00:00, 2019-02-15 00:00:00, 2019-02-22 00:00:00, 2019-03-01 00:00:00, 2019-03-08 00:00:00, 2019-03-15 00:00:00, 2019-03-22 00:00:00, 2019-03-29 00:00:00, 2019-04-05 00:00:00, 2019-04-12 00:00:00, 2019-04-19 00:00:00, 2019-04-26 00:00:00, 2019-05-03 00:00:00, 2019-05-10 00:00:00, 2019-05-17 00:00:00, 2019-05-24 00:00:00, 2019-05-31 00:00:00, 2019-06-07 00:00:00, 2019-06-14 00:00:00, 2019-06-21 00:00:00, 2019-06-28 00:00:00, 2019-07-05 00:00:00, 2019-07-12 00:00:00, 2019-07-19 00:00:00, 2019-07-26 00:00:00, 2019-08-02 00:00:00, 2019-08-09 00:00:00, 2019-08-16 00:00:00, 2019-08-23 00:00:00, 2019-08-30 00:00:00, 2019-09-06 00:00:00, 2019-09-13 00:00:00, 2019-09-20 00:00:00, 2019-09-27 00:00:00, 2019-10-04 00:00:00, 2019-10-11 00:00:00, 2019-10-18 00:00:00, 2019-10-25 00:00:00, 2019-11-01 00:00:00, 2019-11-08 00:00:00, 2019-11-15 00:00:00, 2019-11-22 00:00:00, 2019-11-29 00:00:00, ...]\n",
      "\n",
      "[279 rows x 0 columns]\n",
      "(279, 0)\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1285, in model_forecast\n",
      "    df_forecast = model_forecast(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1389, in model_forecast\n",
      "    model = model.fit(df_train_low, future_regressor_train)\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 821, in fit\n",
      "    self.model = self.model.fit(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\statsmodels.py\", line 2080, in fit\n",
      "    df = self.basic_profile(df)\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\base.py\", line 75, in basic_profile\n",
      "    raise ValueError(f\"{self.name} training dataframe has no data: {df.shape}\")\n",
      "ValueError: ARDL training dataframe has no data: (139, 0)\n",
      "\n",
      "FAILED: Ensemble BestN component 3 of 5 ARDL with error: ValueError('ARDL training dataframe has no data: (139, 0)')\n",
      "ValueError('zero-size array to reduction operation minimum which has no identity')\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [2018-01-05 00:00:00, 2018-01-12 00:00:00, 2018-01-19 00:00:00, 2018-01-26 00:00:00, 2018-02-02 00:00:00, 2018-02-09 00:00:00, 2018-02-16 00:00:00, 2018-02-23 00:00:00, 2018-03-02 00:00:00, 2018-03-09 00:00:00, 2018-03-16 00:00:00, 2018-03-23 00:00:00, 2018-03-30 00:00:00, 2018-04-06 00:00:00, 2018-04-13 00:00:00, 2018-04-20 00:00:00, 2018-04-27 00:00:00, 2018-05-04 00:00:00, 2018-05-11 00:00:00, 2018-05-18 00:00:00, 2018-05-25 00:00:00, 2018-06-01 00:00:00, 2018-06-08 00:00:00, 2018-06-15 00:00:00, 2018-06-22 00:00:00, 2018-06-29 00:00:00, 2018-07-06 00:00:00, 2018-07-13 00:00:00, 2018-07-20 00:00:00, 2018-07-27 00:00:00, 2018-08-03 00:00:00, 2018-08-10 00:00:00, 2018-08-17 00:00:00, 2018-08-24 00:00:00, 2018-08-31 00:00:00, 2018-09-07 00:00:00, 2018-09-14 00:00:00, 2018-09-21 00:00:00, 2018-09-28 00:00:00, 2018-10-05 00:00:00, 2018-10-12 00:00:00, 2018-10-19 00:00:00, 2018-10-26 00:00:00, 2018-11-02 00:00:00, 2018-11-09 00:00:00, 2018-11-16 00:00:00, 2018-11-23 00:00:00, 2018-11-30 00:00:00, 2018-12-07 00:00:00, 2018-12-14 00:00:00, 2018-12-21 00:00:00, 2018-12-28 00:00:00, 2019-01-04 00:00:00, 2019-01-11 00:00:00, 2019-01-18 00:00:00, 2019-01-25 00:00:00, 2019-02-01 00:00:00, 2019-02-08 00:00:00, 2019-02-15 00:00:00, 2019-02-22 00:00:00, 2019-03-01 00:00:00, 2019-03-08 00:00:00, 2019-03-15 00:00:00, 2019-03-22 00:00:00, 2019-03-29 00:00:00, 2019-04-05 00:00:00, 2019-04-12 00:00:00, 2019-04-19 00:00:00, 2019-04-26 00:00:00, 2019-05-03 00:00:00, 2019-05-10 00:00:00, 2019-05-17 00:00:00, 2019-05-24 00:00:00, 2019-05-31 00:00:00, 2019-06-07 00:00:00, 2019-06-14 00:00:00, 2019-06-21 00:00:00, 2019-06-28 00:00:00, 2019-07-05 00:00:00, 2019-07-12 00:00:00, 2019-07-19 00:00:00, 2019-07-26 00:00:00, 2019-08-02 00:00:00, 2019-08-09 00:00:00, 2019-08-16 00:00:00, 2019-08-23 00:00:00, 2019-08-30 00:00:00, 2019-09-06 00:00:00, 2019-09-13 00:00:00, 2019-09-20 00:00:00, 2019-09-27 00:00:00, 2019-10-04 00:00:00, 2019-10-11 00:00:00, 2019-10-18 00:00:00, 2019-10-25 00:00:00, 2019-11-01 00:00:00, 2019-11-08 00:00:00, 2019-11-15 00:00:00, 2019-11-22 00:00:00, 2019-11-29 00:00:00, ...]\n",
      "\n",
      "[279 rows x 0 columns]\n",
      "(279, 0)\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1285, in model_forecast\n",
      "    df_forecast = model_forecast(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1389, in model_forecast\n",
      "    model = model.fit(df_train_low, future_regressor_train)\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 821, in fit\n",
      "    self.model = self.model.fit(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\statsmodels.py\", line 2080, in fit\n",
      "    df = self.basic_profile(df)\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\base.py\", line 75, in basic_profile\n",
      "    raise ValueError(f\"{self.name} training dataframe has no data: {df.shape}\")\n",
      "ValueError: ARDL training dataframe has no data: (279, 0)\n",
      "\n",
      "FAILED: Ensemble BestN component 4 of 5 ARDL with error: ValueError('ARDL training dataframe has no data: (279, 0)')\n",
      "ValueError('zero-size array to reduction operation minimum which has no identity')\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [2018-01-05 00:00:00, 2018-01-12 00:00:00, 2018-01-19 00:00:00, 2018-01-26 00:00:00, 2018-02-02 00:00:00, 2018-02-09 00:00:00, 2018-02-16 00:00:00, 2018-02-23 00:00:00, 2018-03-02 00:00:00, 2018-03-09 00:00:00, 2018-03-16 00:00:00, 2018-03-23 00:00:00, 2018-03-30 00:00:00, 2018-04-06 00:00:00, 2018-04-13 00:00:00, 2018-04-20 00:00:00, 2018-04-27 00:00:00, 2018-05-04 00:00:00, 2018-05-11 00:00:00, 2018-05-18 00:00:00, 2018-05-25 00:00:00, 2018-06-01 00:00:00, 2018-06-08 00:00:00, 2018-06-15 00:00:00, 2018-06-22 00:00:00, 2018-06-29 00:00:00, 2018-07-06 00:00:00, 2018-07-13 00:00:00, 2018-07-20 00:00:00, 2018-07-27 00:00:00, 2018-08-03 00:00:00, 2018-08-10 00:00:00, 2018-08-17 00:00:00, 2018-08-24 00:00:00, 2018-08-31 00:00:00, 2018-09-07 00:00:00, 2018-09-14 00:00:00, 2018-09-21 00:00:00, 2018-09-28 00:00:00, 2018-10-05 00:00:00, 2018-10-12 00:00:00, 2018-10-19 00:00:00, 2018-10-26 00:00:00, 2018-11-02 00:00:00, 2018-11-09 00:00:00, 2018-11-16 00:00:00, 2018-11-23 00:00:00, 2018-11-30 00:00:00, 2018-12-07 00:00:00, 2018-12-14 00:00:00, 2018-12-21 00:00:00, 2018-12-28 00:00:00, 2019-01-04 00:00:00, 2019-01-11 00:00:00, 2019-01-18 00:00:00, 2019-01-25 00:00:00, 2019-02-01 00:00:00, 2019-02-08 00:00:00, 2019-02-15 00:00:00, 2019-02-22 00:00:00, 2019-03-01 00:00:00, 2019-03-08 00:00:00, 2019-03-15 00:00:00, 2019-03-22 00:00:00, 2019-03-29 00:00:00, 2019-04-05 00:00:00, 2019-04-12 00:00:00, 2019-04-19 00:00:00, 2019-04-26 00:00:00, 2019-05-03 00:00:00, 2019-05-10 00:00:00, 2019-05-17 00:00:00, 2019-05-24 00:00:00, 2019-05-31 00:00:00, 2019-06-07 00:00:00, 2019-06-14 00:00:00, 2019-06-21 00:00:00, 2019-06-28 00:00:00, 2019-07-05 00:00:00, 2019-07-12 00:00:00, 2019-07-19 00:00:00, 2019-07-26 00:00:00, 2019-08-02 00:00:00, 2019-08-09 00:00:00, 2019-08-16 00:00:00, 2019-08-23 00:00:00, 2019-08-30 00:00:00, 2019-09-06 00:00:00, 2019-09-13 00:00:00, 2019-09-20 00:00:00, 2019-09-27 00:00:00, 2019-10-04 00:00:00, 2019-10-11 00:00:00, 2019-10-18 00:00:00, 2019-10-25 00:00:00, 2019-11-01 00:00:00, 2019-11-08 00:00:00, 2019-11-15 00:00:00, 2019-11-22 00:00:00, 2019-11-29 00:00:00, ...]\n",
      "\n",
      "[279 rows x 0 columns]\n",
      "(279, 0)\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1285, in model_forecast\n",
      "    df_forecast = model_forecast(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1389, in model_forecast\n",
      "    model = model.fit(df_train_low, future_regressor_train)\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 821, in fit\n",
      "    self.model = self.model.fit(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\statsmodels.py\", line 2080, in fit\n",
      "    df = self.basic_profile(df)\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\base.py\", line 75, in basic_profile\n",
      "    raise ValueError(f\"{self.name} training dataframe has no data: {df.shape}\")\n",
      "ValueError: ARDL training dataframe has no data: (279, 0)\n",
      "\n",
      "FAILED: Ensemble BestN component 5 of 5 ARDL with error: ValueError('ARDL training dataframe has no data: (279, 0)')\n",
      "ValueError('zero-size array to reduction operation minimum which has no identity')\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [2018-01-05 00:00:00, 2018-01-12 00:00:00, 2018-01-19 00:00:00, 2018-01-26 00:00:00, 2018-02-02 00:00:00, 2018-02-09 00:00:00, 2018-02-16 00:00:00, 2018-02-23 00:00:00, 2018-03-02 00:00:00, 2018-03-09 00:00:00, 2018-03-16 00:00:00, 2018-03-23 00:00:00, 2018-03-30 00:00:00, 2018-04-06 00:00:00, 2018-04-13 00:00:00, 2018-04-20 00:00:00, 2018-04-27 00:00:00, 2018-05-04 00:00:00, 2018-05-11 00:00:00, 2018-05-18 00:00:00, 2018-05-25 00:00:00, 2018-06-01 00:00:00, 2018-06-08 00:00:00, 2018-06-15 00:00:00, 2018-06-22 00:00:00, 2018-06-29 00:00:00, 2018-07-06 00:00:00, 2018-07-13 00:00:00, 2018-07-20 00:00:00, 2018-07-27 00:00:00, 2018-08-03 00:00:00, 2018-08-10 00:00:00, 2018-08-17 00:00:00, 2018-08-24 00:00:00, 2018-08-31 00:00:00, 2018-09-07 00:00:00, 2018-09-14 00:00:00, 2018-09-21 00:00:00, 2018-09-28 00:00:00, 2018-10-05 00:00:00, 2018-10-12 00:00:00, 2018-10-19 00:00:00, 2018-10-26 00:00:00, 2018-11-02 00:00:00, 2018-11-09 00:00:00, 2018-11-16 00:00:00, 2018-11-23 00:00:00, 2018-11-30 00:00:00, 2018-12-07 00:00:00, 2018-12-14 00:00:00, 2018-12-21 00:00:00, 2018-12-28 00:00:00, 2019-01-04 00:00:00, 2019-01-11 00:00:00, 2019-01-18 00:00:00, 2019-01-25 00:00:00, 2019-02-01 00:00:00, 2019-02-08 00:00:00, 2019-02-15 00:00:00, 2019-02-22 00:00:00, 2019-03-01 00:00:00, 2019-03-08 00:00:00, 2019-03-15 00:00:00, 2019-03-22 00:00:00, 2019-03-29 00:00:00, 2019-04-05 00:00:00, 2019-04-12 00:00:00, 2019-04-19 00:00:00, 2019-04-26 00:00:00, 2019-05-03 00:00:00, 2019-05-10 00:00:00, 2019-05-17 00:00:00, 2019-05-24 00:00:00, 2019-05-31 00:00:00, 2019-06-07 00:00:00, 2019-06-14 00:00:00, 2019-06-21 00:00:00, 2019-06-28 00:00:00, 2019-07-05 00:00:00, 2019-07-12 00:00:00, 2019-07-19 00:00:00, 2019-07-26 00:00:00, 2019-08-02 00:00:00, 2019-08-09 00:00:00, 2019-08-16 00:00:00, 2019-08-23 00:00:00, 2019-08-30 00:00:00, 2019-09-06 00:00:00, 2019-09-13 00:00:00, 2019-09-20 00:00:00, 2019-09-27 00:00:00, 2019-10-04 00:00:00, 2019-10-11 00:00:00, 2019-10-18 00:00:00, 2019-10-25 00:00:00, 2019-11-01 00:00:00, 2019-11-08 00:00:00, 2019-11-15 00:00:00, 2019-11-22 00:00:00, 2019-11-29 00:00:00, ...]\n",
      "\n",
      "[279 rows x 0 columns]\n",
      "(279, 0)\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1285, in model_forecast\n",
      "    df_forecast = model_forecast(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1389, in model_forecast\n",
      "    model = model.fit(df_train_low, future_regressor_train)\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 821, in fit\n",
      "    self.model = self.model.fit(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\statsmodels.py\", line 1881, in fit\n",
      "    df = self.basic_profile(df)\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\base.py\", line 75, in basic_profile\n",
      "    raise ValueError(f\"{self.name} training dataframe has no data: {df.shape}\")\n",
      "ValueError: Theta training dataframe has no data: (279, 0)\n",
      "\n",
      "FAILED: Ensemble BestN component 6 of 5 Theta with error: ValueError('Theta training dataframe has no data: (279, 0)')\n",
      "ValueError('zero-size array to reduction operation minimum which has no identity')\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [2018-01-05 00:00:00, 2018-01-12 00:00:00, 2018-01-19 00:00:00, 2018-01-26 00:00:00, 2018-02-02 00:00:00, 2018-02-09 00:00:00, 2018-02-16 00:00:00, 2018-02-23 00:00:00, 2018-03-02 00:00:00, 2018-03-09 00:00:00, 2018-03-16 00:00:00, 2018-03-23 00:00:00, 2018-03-30 00:00:00, 2018-04-06 00:00:00, 2018-04-13 00:00:00, 2018-04-20 00:00:00, 2018-04-27 00:00:00, 2018-05-04 00:00:00, 2018-05-11 00:00:00, 2018-05-18 00:00:00, 2018-05-25 00:00:00, 2018-06-01 00:00:00, 2018-06-08 00:00:00, 2018-06-15 00:00:00, 2018-06-22 00:00:00, 2018-06-29 00:00:00, 2018-07-06 00:00:00, 2018-07-13 00:00:00, 2018-07-20 00:00:00, 2018-07-27 00:00:00, 2018-08-03 00:00:00, 2018-08-10 00:00:00, 2018-08-17 00:00:00, 2018-08-24 00:00:00, 2018-08-31 00:00:00, 2018-09-07 00:00:00, 2018-09-14 00:00:00, 2018-09-21 00:00:00, 2018-09-28 00:00:00, 2018-10-05 00:00:00, 2018-10-12 00:00:00, 2018-10-19 00:00:00, 2018-10-26 00:00:00, 2018-11-02 00:00:00, 2018-11-09 00:00:00, 2018-11-16 00:00:00, 2018-11-23 00:00:00, 2018-11-30 00:00:00, 2018-12-07 00:00:00, 2018-12-14 00:00:00, 2018-12-21 00:00:00, 2018-12-28 00:00:00, 2019-01-04 00:00:00, 2019-01-11 00:00:00, 2019-01-18 00:00:00, 2019-01-25 00:00:00, 2019-02-01 00:00:00, 2019-02-08 00:00:00, 2019-02-15 00:00:00, 2019-02-22 00:00:00, 2019-03-01 00:00:00, 2019-03-08 00:00:00, 2019-03-15 00:00:00, 2019-03-22 00:00:00, 2019-03-29 00:00:00, 2019-04-05 00:00:00, 2019-04-12 00:00:00, 2019-04-19 00:00:00, 2019-04-26 00:00:00, 2019-05-03 00:00:00, 2019-05-10 00:00:00, 2019-05-17 00:00:00, 2019-05-24 00:00:00, 2019-05-31 00:00:00, 2019-06-07 00:00:00, 2019-06-14 00:00:00, 2019-06-21 00:00:00, 2019-06-28 00:00:00, 2019-07-05 00:00:00, 2019-07-12 00:00:00, 2019-07-19 00:00:00, 2019-07-26 00:00:00, 2019-08-02 00:00:00, 2019-08-09 00:00:00, 2019-08-16 00:00:00, 2019-08-23 00:00:00, 2019-08-30 00:00:00, 2019-09-06 00:00:00, 2019-09-13 00:00:00, 2019-09-20 00:00:00, 2019-09-27 00:00:00, 2019-10-04 00:00:00, 2019-10-11 00:00:00, 2019-10-18 00:00:00, 2019-10-25 00:00:00, 2019-11-01 00:00:00, 2019-11-08 00:00:00, 2019-11-15 00:00:00, 2019-11-22 00:00:00, 2019-11-29 00:00:00, ...]\n",
      "\n",
      "[279 rows x 0 columns]\n",
      "(279, 0)\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1285, in model_forecast\n",
      "    df_forecast = model_forecast(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1389, in model_forecast\n",
      "    model = model.fit(df_train_low, future_regressor_train)\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 821, in fit\n",
      "    self.model = self.model.fit(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\statsmodels.py\", line 2080, in fit\n",
      "    df = self.basic_profile(df)\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\base.py\", line 75, in basic_profile\n",
      "    raise ValueError(f\"{self.name} training dataframe has no data: {df.shape}\")\n",
      "ValueError: ARDL training dataframe has no data: (139, 0)\n",
      "\n",
      "FAILED: Ensemble BestN component 2 of 5 ARDL with error: ValueError('ARDL training dataframe has no data: (139, 0)')\n",
      "ValueError('zero-size array to reduction operation minimum which has no identity')\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [2018-01-05 00:00:00, 2018-01-12 00:00:00, 2018-01-19 00:00:00, 2018-01-26 00:00:00, 2018-02-02 00:00:00, 2018-02-09 00:00:00, 2018-02-16 00:00:00, 2018-02-23 00:00:00, 2018-03-02 00:00:00, 2018-03-09 00:00:00, 2018-03-16 00:00:00, 2018-03-23 00:00:00, 2018-03-30 00:00:00, 2018-04-06 00:00:00, 2018-04-13 00:00:00, 2018-04-20 00:00:00, 2018-04-27 00:00:00, 2018-05-04 00:00:00, 2018-05-11 00:00:00, 2018-05-18 00:00:00, 2018-05-25 00:00:00, 2018-06-01 00:00:00, 2018-06-08 00:00:00, 2018-06-15 00:00:00, 2018-06-22 00:00:00, 2018-06-29 00:00:00, 2018-07-06 00:00:00, 2018-07-13 00:00:00, 2018-07-20 00:00:00, 2018-07-27 00:00:00, 2018-08-03 00:00:00, 2018-08-10 00:00:00, 2018-08-17 00:00:00, 2018-08-24 00:00:00, 2018-08-31 00:00:00, 2018-09-07 00:00:00, 2018-09-14 00:00:00, 2018-09-21 00:00:00, 2018-09-28 00:00:00, 2018-10-05 00:00:00, 2018-10-12 00:00:00, 2018-10-19 00:00:00, 2018-10-26 00:00:00, 2018-11-02 00:00:00, 2018-11-09 00:00:00, 2018-11-16 00:00:00, 2018-11-23 00:00:00, 2018-11-30 00:00:00, 2018-12-07 00:00:00, 2018-12-14 00:00:00, 2018-12-21 00:00:00, 2018-12-28 00:00:00, 2019-01-04 00:00:00, 2019-01-11 00:00:00, 2019-01-18 00:00:00, 2019-01-25 00:00:00, 2019-02-01 00:00:00, 2019-02-08 00:00:00, 2019-02-15 00:00:00, 2019-02-22 00:00:00, 2019-03-01 00:00:00, 2019-03-08 00:00:00, 2019-03-15 00:00:00, 2019-03-22 00:00:00, 2019-03-29 00:00:00, 2019-04-05 00:00:00, 2019-04-12 00:00:00, 2019-04-19 00:00:00, 2019-04-26 00:00:00, 2019-05-03 00:00:00, 2019-05-10 00:00:00, 2019-05-17 00:00:00, 2019-05-24 00:00:00, 2019-05-31 00:00:00, 2019-06-07 00:00:00, 2019-06-14 00:00:00, 2019-06-21 00:00:00, 2019-06-28 00:00:00, 2019-07-05 00:00:00, 2019-07-12 00:00:00, 2019-07-19 00:00:00, 2019-07-26 00:00:00, 2019-08-02 00:00:00, 2019-08-09 00:00:00, 2019-08-16 00:00:00, 2019-08-23 00:00:00, 2019-08-30 00:00:00, 2019-09-06 00:00:00, 2019-09-13 00:00:00, 2019-09-20 00:00:00, 2019-09-27 00:00:00, 2019-10-04 00:00:00, 2019-10-11 00:00:00, 2019-10-18 00:00:00, 2019-10-25 00:00:00, 2019-11-01 00:00:00, 2019-11-08 00:00:00, 2019-11-15 00:00:00, 2019-11-22 00:00:00, 2019-11-29 00:00:00, ...]\n",
      "\n",
      "[279 rows x 0 columns]\n",
      "(279, 0)\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1285, in model_forecast\n",
      "    df_forecast = model_forecast(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1389, in model_forecast\n",
      "    model = model.fit(df_train_low, future_regressor_train)\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 821, in fit\n",
      "    self.model = self.model.fit(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\statsmodels.py\", line 2080, in fit\n",
      "    df = self.basic_profile(df)\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\base.py\", line 75, in basic_profile\n",
      "    raise ValueError(f\"{self.name} training dataframe has no data: {df.shape}\")\n",
      "ValueError: ARDL training dataframe has no data: (279, 0)\n",
      "\n",
      "FAILED: Ensemble BestN component 3 of 5 ARDL with error: ValueError('ARDL training dataframe has no data: (279, 0)')\n",
      "ValueError('zero-size array to reduction operation minimum which has no identity')\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [2018-01-05 00:00:00, 2018-01-12 00:00:00, 2018-01-19 00:00:00, 2018-01-26 00:00:00, 2018-02-02 00:00:00, 2018-02-09 00:00:00, 2018-02-16 00:00:00, 2018-02-23 00:00:00, 2018-03-02 00:00:00, 2018-03-09 00:00:00, 2018-03-16 00:00:00, 2018-03-23 00:00:00, 2018-03-30 00:00:00, 2018-04-06 00:00:00, 2018-04-13 00:00:00, 2018-04-20 00:00:00, 2018-04-27 00:00:00, 2018-05-04 00:00:00, 2018-05-11 00:00:00, 2018-05-18 00:00:00, 2018-05-25 00:00:00, 2018-06-01 00:00:00, 2018-06-08 00:00:00, 2018-06-15 00:00:00, 2018-06-22 00:00:00, 2018-06-29 00:00:00, 2018-07-06 00:00:00, 2018-07-13 00:00:00, 2018-07-20 00:00:00, 2018-07-27 00:00:00, 2018-08-03 00:00:00, 2018-08-10 00:00:00, 2018-08-17 00:00:00, 2018-08-24 00:00:00, 2018-08-31 00:00:00, 2018-09-07 00:00:00, 2018-09-14 00:00:00, 2018-09-21 00:00:00, 2018-09-28 00:00:00, 2018-10-05 00:00:00, 2018-10-12 00:00:00, 2018-10-19 00:00:00, 2018-10-26 00:00:00, 2018-11-02 00:00:00, 2018-11-09 00:00:00, 2018-11-16 00:00:00, 2018-11-23 00:00:00, 2018-11-30 00:00:00, 2018-12-07 00:00:00, 2018-12-14 00:00:00, 2018-12-21 00:00:00, 2018-12-28 00:00:00, 2019-01-04 00:00:00, 2019-01-11 00:00:00, 2019-01-18 00:00:00, 2019-01-25 00:00:00, 2019-02-01 00:00:00, 2019-02-08 00:00:00, 2019-02-15 00:00:00, 2019-02-22 00:00:00, 2019-03-01 00:00:00, 2019-03-08 00:00:00, 2019-03-15 00:00:00, 2019-03-22 00:00:00, 2019-03-29 00:00:00, 2019-04-05 00:00:00, 2019-04-12 00:00:00, 2019-04-19 00:00:00, 2019-04-26 00:00:00, 2019-05-03 00:00:00, 2019-05-10 00:00:00, 2019-05-17 00:00:00, 2019-05-24 00:00:00, 2019-05-31 00:00:00, 2019-06-07 00:00:00, 2019-06-14 00:00:00, 2019-06-21 00:00:00, 2019-06-28 00:00:00, 2019-07-05 00:00:00, 2019-07-12 00:00:00, 2019-07-19 00:00:00, 2019-07-26 00:00:00, 2019-08-02 00:00:00, 2019-08-09 00:00:00, 2019-08-16 00:00:00, 2019-08-23 00:00:00, 2019-08-30 00:00:00, 2019-09-06 00:00:00, 2019-09-13 00:00:00, 2019-09-20 00:00:00, 2019-09-27 00:00:00, 2019-10-04 00:00:00, 2019-10-11 00:00:00, 2019-10-18 00:00:00, 2019-10-25 00:00:00, 2019-11-01 00:00:00, 2019-11-08 00:00:00, 2019-11-15 00:00:00, 2019-11-22 00:00:00, 2019-11-29 00:00:00, ...]\n",
      "\n",
      "[279 rows x 0 columns]\n",
      "(279, 0)\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1285, in model_forecast\n",
      "    df_forecast = model_forecast(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 1389, in model_forecast\n",
      "    model = model.fit(df_train_low, future_regressor_train)\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\evaluator\\auto_model.py\", line 821, in fit\n",
      "    self.model = self.model.fit(\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\statsmodels.py\", line 2080, in fit\n",
      "    df = self.basic_profile(df)\n",
      "  File \"C:\\Users\\ekaushik\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\autots\\models\\base.py\", line 75, in basic_profile\n",
      "    raise ValueError(f\"{self.name} training dataframe has no data: {df.shape}\")\n",
      "ValueError: ARDL training dataframe has no data: (279, 0)\n",
      "\n",
      "FAILED: Ensemble BestN component 4 of 5 ARDL with error: ValueError('ARDL training dataframe has no data: (279, 0)')\n"
     ]
    }
   ],
   "source": [
    "model = model.fit(\n",
    "    df_all[df_all.Date < dt],\n",
    "    date_col='Date' if long else None,\n",
    "    value_col='Value' if long else None,\n",
    "    id_col='Category' if long else None,\n",
    ")\n",
    "\n",
    "prediction = model.predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecast Values:\n",
      "                      A            B            C            D            E\n",
      "2023-05-12  2993.960890  1358.338892  3215.706000  1606.017252  3633.462565\n",
      "2023-05-19  2993.301672  1364.228660  3321.606063  1603.854021  3671.168153\n",
      "2023-05-26  2998.944101  1368.878090  3268.348930  1601.389840  3674.970580\n",
      "2023-06-02  2996.393678  1372.714653  3228.904301  1598.190561  3681.158883\n",
      "2023-06-09  2995.009109  1376.730490  3258.438560  1595.231997  3691.765487\n",
      "2023-06-16  2995.061205  1380.459155  3326.437354  1592.514988  3699.815669\n",
      "2023-06-23  2993.335429  1383.843069  3256.616285  1589.954888  3707.482041\n",
      "2023-06-30  2992.243291  1387.050040  3233.771640  1587.505977  3715.208074\n",
      "2023-07-07  2991.541565  1390.049794  3289.544114  1585.176867  3722.480417\n",
      "2023-07-14  2990.547936  1392.828075  3337.899398  1582.971247  3729.427273\n",
      "2023-07-21  2989.851319  1395.416158  3280.291731  1580.885428  3736.110989\n",
      "2023-07-28  2989.313286  1397.822599  3272.773144  1578.913959  3742.506202\n",
      "2023-08-04  2988.788622  1400.052005  3325.285752  1577.052458  3748.629676\n",
      "2023-08-11  2988.409737  1402.115911  3361.541305  1575.296959  3754.497441\n",
      "2023-08-18  2988.119501  1404.023577  3309.373961  1573.643436  3760.117802\n",
      "'2023-08-25'\n",
      "'2023-08-25'\n",
      "'2023-08-25'\n",
      "'2023-08-25'\n",
      "'2023-08-25'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5r0H9CyGEEEIIIURDjRgxgqFDh1723N9//41KpeLIkSNNHJUwpjpNTbe1tSU8PLzGMWtra5ydnQkPDycuLo5ly5Zx++234+zszJEjR5g+fTp9+/Y1bHM2ZMgQwsLCeOSRR3j//fdJT0/nlVdeYerUqZibmwMwZcoUPv/8c1588UXGjx/P5s2bWb58OWvXrq33Ez1fUMaYr/eSkldCkKs1303ogb1l1Xrwf0bE82QrMyGEEEIIIUSzmjBhAqNGjeLcuXP4+PjUOLdo0SK6det22W2kxfWjXlXTr8TMzIyNGzcyZMgQ2rZty3PPPceoUaNYs2aNoY1Go+H3339Ho9EQERHBmDFjGDt2bI19xwMDA1m7di0bNmygY8eOfPTRR3z99df13kM8v7iCR77ZS3xWEd4Olnw/oScuNuaG8+287DFRq8gqLOdcbkn9XwAhhBBCCCFEi6YoCsXllU3+pShKrWO84447cHV1ZfHixTWOFxYWsmLFCu666y4eeughvL29sbKyon379vzwww9X7VOlUvHrr7/WOObg4FDjHsnJydx///04ODjg5OTEyJEjSUhIqHXcovbqXDX937Zu3Wr43tfXl23btl3zGn9/f/7444+rtunfvz+HDh1qaHgUlVXy5Pf7OJlegIuNOd9P7ImXQ8115hamGkI97Tiakk90ch6+TrLPoBBCCCGEEDeikgodYa+ua/L7Hp8ViZVZ7dIvExMTxo4dy+LFi3n55ZcNM3ZXrFiBTqdjzJgxrFixgpdeegk7OzvWrl3LI488QnBwMD169KhXfBUVFURGRhIREcHff/+NiYkJb775JkOHDuXIkSOYmZnVq19xeUYdEW+JnvnhEIeS8rC3NOX7iT0IdLG+bLuLp6cLIYQQQgghRHMaP348cXFxNQY6Fy1axKhRo/D39+f555+nU6dOBAUF8fTTTzN06FCWL19e7/v99NNP6PV6vv76a9q3b09oaCiLFi0iKSmpxuCrMI4Gj4i3dHvjc7C1tWXJ+B609bh8JXWATr4OfLs7kUNJUjldCCGEEEKIG5WlqYbjs+q35LWh962Ltm3b0rt3bxYuXEj//v2JjY3l77//ZtasWeh0Ot5++22WL19OSkoK5eXllJWVYWVV/5m9hw8fJjY2Fltb2xrHS0tLiYuLq3e/4vJu+ETczETN1492N1RGv5Lq88dStZRX6jEzueEnCwghhBBCCHHTUalUtZ4i3twmTJjA008/zdy5c1m0aBHBwcH069eP9957jzlz5vDpp5/Svn17rK2tmTZtGuXl5VfsS6VSXbJOvaKiwvB9YWEhXbt2ZenSpZdc6+rqarwnJYCbYGr6Jw90JCLY+ZrtAl2ssbc0pbxSz8l0bRNEJloibWkFSdnF5JdU1KmghrEoikJBafPcWwghhBBCtCz3338/arWaZcuW8e233zJ+/HhUKhU7d+5k5MiRjBkzho4dOxIUFMTp06ev2perqytpaWmGx2fOnKG4uNjwuEuXLpw5cwY3NzdCQkJqfNnb2zfac7xZXR8fBTVAv9ZutWqnUqno6OvA9tPniU7Oo4OPQ+MGJmpQFIXnlh/m8Lk8Jt0axL1dfTDRNO3nRMk5xdz5+Q5yi6s+GdSoVdhbmuJgaYq9lSmOVmaG7x0szXC0Nq06b2WG44Vj9lam2JqboFZfeQs8nV4hLb+EpOxiEnOKScwuJimnqOrP7GIKyiq5u7M3nzzQqYmeuRBCCCGEaIlsbGx44IEHmDlzJlqtlnHjxgHQqlUrVq5cya5du3B0dOTjjz8mIyODsLCwK/Y1YMAAPv/8cyIiItDpdLz00kuYmpoazo8ePZoPPviAkSNHMmvWLHx8fEhMTOSXX37hxRdfvGQbNdEwN3wiXhedqhPxpDzGRjR3NDeXdTEZ/HIoBYD/++UoX/59lheGtGFouEeT7OteXqnnqWUHyS2uQK0CvVKVMOcUlZNTdOUpPpdzuQTe3tKUvOJyEnOKOZdTQrlOf9U+fj+Sypt3hWNtLr+iQgghhBA3swkTJvDNN99w++234+XlBcArr7zC2bNniYyMxMrKismTJ3PXXXeRn59/xX4++ugjHnvsMW699Va8vLyYM2cOBw4cMJy3srJi+/btvPTSS9xzzz0UFBTg7e3NwIEDsbO7cq0tUT8q5QadA6vVarG3tyc/P7/WPzhbTmby2OIoglys2fx8/8YNUBiUVeoY/PF2knKKubWVC8dS8g2j0h197HlxaFv6hLg0agyz1hxn4c547C1N+ePZW3G2NiO/pIK84gryisvJLa4gv6S86vFFx/95XPV9SYWuVvcz1ajwdbTCz9kKfycr/Jyt8Xeywt/ZiscWR3Eut4RvHu3GwFD3Rn3eQgghhBA3stLSUuLj4wkMDMTCwqK5wxE3iKv9XNU2D5XhtotUF2w7m1VEXnE5DlayV15TWLwzgaScYtztzFkwpit6ReGrv+P5+u+zHD6Xz+iv93JrKxdejGxLex/jr09ZH5POwp3xAHx4X0e8L+wzb2Gqwd2ubv9gl1bo0JZUkFudqF+UpNtamOLvbIWfkxVeDpZorjB9vW9rV5btTWL76fOSiAshhBBCCHEDkkT8Io7WZgQ4W5GQXUx0ch7929Rufbmov/MFZXy2ORaAFyPbGqZizxjcmrER/ny+OZalexP5+0wWf5/ZwfAOnjw3uDVBrjZGuf+53GKeX3EYgAm3BDI4rGGJr4WpBgtTDW51TOAv1rfVhUT8TFaDYhFCCCGEEEK0TDd81fS6qh4Vj07Oa9Y4bhYfbzhFYVklHXzsubuzd41zLjbmvH5nOzY/15+7O3ujUsHaI2kM/mQ7M385Snp+aYPuXaHT8/QPh9CWVtLR14GXhrZtUH/G0jvEGY1aRXxWEck5xde+QAghhBBCCHFdkUT8XyQRbzoxqfn8GJUMwKt3hF2x0rivkxWfPNCJP565lQFt3dDpFX7Yl8Sgj7fx17H0et//g3WnOJSUh62FCZ8/1LnF7B1vZ2FKFz8HALadPt+8wQghhBBCCCGMrmVkHi1IJz9HAA4n58lezo1IURRm/34cRYE7OnjSLcDpmteEetqxcFx3lj8eQUdfBwrLKpny/QE+WHcSnb5uf1ebT2bw5fazAHxwb0d8nazq9TwaS99WrgBsl0RcCCGEEEKIG44k4v8S6mmLmUZNbnEFidkyLbixrD+ewZ6zOZibqPm/YXWbEt4j0ImVUyIY3ycQgLlb4hi3aB+5tdxmLDWvhBnLq9aFj+sdwNBwj7oF3wT6tq5KxHfFZVNxja3OhBBCCCGEENcXScT/xdxEQzvvqjLzMj29cZRV6nj7jxMATO4bhI9j3UejTTVqXh0RxpwHO2FhqubvM1mM+HwHx1KuvHciVK0Lf+aHQ+QVV9De256Zt7eMdeH/Fu5tj6OVKYVllRxKymvucIQQQgghhBBGJIn4Zcg68ca1eGcCidnFuNmaM6VfcIP6GtnJm1VP9sHPyYpzuSWMmr+LVYfOXbH9xxtOsz8xF1tzEz5/uDPmJpoG3b+xaNQqbpHp6UIIIYQQQtyQJBG/jOpE/FBSbvMGcgOqsV3Z0H+2K2uIUE871jx1C/3buFJWqWf6T4d5fXXMJVO6t57KZP7WOADeHdUBf2frBt+7MfVt5QLA9jOSiAshhBBCCHEjkUT8Mjr7VhVsO56mpbRC18zR3Fg+3nCawrJK2nvbc8+/titrCHsrU755tDtPDwgBYPGuBEZ/tZfMgqotztLzSw3rwsf08mN4B0+j3buxVK8TP5qST04t178LIYQQQgiRkJCASqUiOjq61tcsXrwYBweHZo+jIbZu3YpKpSIvL69J7tcQkohfhq+TJU7WZlToFI6naZs7nBvG8VQtP0UlAfDqiCtvV1ZfGrWK54a04ctHumJjbsK+hBxGfLaDqIQcnvnxEDlF5YR52vHK8DCj3rexuNtZ0NbDFkWBv2VUXAghhBDippOcnMz48ePx8vLCzMwMf39/nn32WbKzs696na+vL2lpaYSHh9f6Xg888ACnT59uaMj1cuDAAVQqFXv27Lns+YEDB3LPPfc0cVSNSxLxy1CpVP+sE5dCWUZRvV2ZXoHhHTzpXovtyuprSDsPfnuqDyFuNmRoy7hvwW72xedgbaZh7uguWJi2zHXhl1M9Kr79dFYzRyKEEEIIIZrS2bNn6datG2fOnOGHH34gNjaWBQsWsGnTJiIiIsjJybnsdeXl5Wg0Gjw8PDAxqf0yUEtLS9zc3IwVfp107dqVjh07snDhwkvOJSQksGXLFiZMmNAMkTWeBiXi7777LiqVimnTpgGQk5PD008/TZs2bbC0tMTPz49nnnmG/PyalaxVKtUlXz/++GONNlu3bqVLly6Ym5sTEhLC4sWLGxJqnUnBNuNafzyD3WezMTNRM7OO25XVR7CrDb9O7cOwi7Yme/ue9gS6tOx14f9WvZ/432fOy772QgghhBDGoChQXtT0X3V8Lzd16lTMzMxYv349/fr1w8/Pj2HDhrFx40ZSUlJ4+eWXAQgICGD27NmMHTsWOzs7Jk+efNkp4atXr6ZVq1ZYWFhw2223sWTJkhrTuP89Nf3111+nU6dOfPfddwQEBGBvb8+DDz5IQUGBoc1ff/3FLbfcgoODA87Oztxxxx3ExcXV669lwoQJ/PTTTxQX19xCevHixXh6ejJ06FC+++47unXrhq2tLR4eHjz88MNkZmZesc/q53CxTz/9lICAgBrHvv76a0JDQ7GwsKBt27bMmzevXs+hLupdKSsqKoovvviCDh06GI6lpqaSmprKhx9+SFhYGImJiUyZMoXU1FRWrlxZ4/pFixYxdOhQw+OL/9Lj4+MZPnw4U6ZMYenSpWzatImJEyfi6elJZGRkfUOuk85+VfFIIt5wNbYru7V+25XVh425CfNGd+HX6BRUqBjZyXhr0ptKtwBHLEzVZBaUcTK9gFBPu+YOSQghhBDi+lZRDG97Nf19/5MKZrUbFMrJyWHdunW89dZbWFpa1jjn4eHB6NGj+emnnwwJ44cffsirr77Ka6+9dtn+4uPjuffee3n22WeZOHEihw4d4vnnn79mHHFxcfz666/8/vvv5Obmcv/99/Puu+/y1ltvAVBUVMSMGTPo0KEDhYWFvPrqq9x9991ER0ejVtdtzHf06NG88MILrFy5krFjxwJVs2qXLFnCuHHj0Gg0VFRUMHv2bNq0aUNmZiYzZsxg3Lhx/PHHH3W618WWLl3Kq6++yueff07nzp05dOgQkyZNwtramkcffbTe/V5LvRLxwsJCRo8ezVdffcWbb75pOB4eHs7PP/9seBwcHMxbb73FmDFjqKysrDE1wsHBAQ8PDy5nwYIFBAYG8tFHHwEQGhrKjh07+OSTT5osEe/g4wBAUk4x2YVlONuYN8l9jSklr4Sj5/IYEuZh9PXYdbFkV9V2Za625jzRv2HbldWVSqXi7s4+TXpPY7Iw1dAryJmtp86z/fR5ScSFEEIIIW4CZ86cQVEUQkNDL3s+NDSU3Nxczp+vqiM0YMAAnnvuOcP5hISEGu2/+OIL2rRpwwcffABAmzZtOHbsmCGhvhK9Xs/ixYuxtbUF4JFHHmHTpk2G60aNGlWj/cKFC3F1deX48eN1Wp8O4OTkxN13383ChQsNifiWLVtISEjgscceA2D8+PGG9kFBQfzvf/+je/fuFBYWYmNjU6f7VXvttdf46KOPDGvQAwMDOX78OF988UXLS8SnTp3K8OHDGTRoUI1E/HLy8/Oxs7O7ZH3C1KlTmThxIkFBQUyZMoXHHnsMlaoqWdy9ezeDBg2q0T4yMtIwBf5yysrKKCsrMzzWahtWZM3e0pRgV2vizhcRnZzHwFD3BvXXlPR6he/2JPLeXycpLtfxn9vbMrlv0ybA1bIKy/hs04XtyiLbGGW7sptN31auVYn4mfM83sB914UQQgghbnqmVlWj081x3zqq7dLEbt26XfX8qVOn6N69e41jPXr0uGa/AQEBhiQcwNPTs8ZU8DNnzvDqq6+yd+9esrKy0Ourtg9OSkqqcyIOVYl2ZGQkcXFxBAcHs3DhQvr160dISNXOSAcOHOD111/n8OHD5Obm1rhfWFjdCzIXFRURFxfHhAkTmDRpkuF4ZWUl9vb2de6vLuqcFf34448cPHiQqKioa7bNyspi9uzZTJ48ucbxWbNmMWDAAKysrFi/fj1PPvkkhYWFPPPMMwCkp6fj7l4z8XV3d0er1VJSUnLJ9AyAd955hzfeeKOuT+eqOvk6XneJeEJWES/+fIR98f8Ub1i0M4HH+gRiqmn62nyfbDhNQVkl4d52jOpy/Y5MN6fqgm1R8bkUl1diZSYfZgghhBBC1JtKVesp4s0lJCQElUrFiRMnuPvuuy85f+LECRwdHXF1rXqfaG3dOM/H1NS0xmOVSmVIfgFGjBiBv78/X331FV5eXuj1esLDwykvr9/WuwMHDsTPz4/Fixfzwgsv8Msvv/DFF18AVUlzZGQkkZGRLF26FFdXV5KSkoiMjLzi/dRq9SUfZlRUVBi+LywsBOCrr76iZ8+eNdppNI1b4LlO7+iTk5N59tln2bBhAxYWFldtq9VqGT58OGFhYbz++us1zv33v/81fN+5c2eKior44IMPDIl4fcycOZMZM2bUuL+vr2+9+wPo5OfAzwfPXRfrxHV6hUU74/lw/SlKK/RYmWl4IbINc7fEkpZfyl/H0hnRsWnXwuQVl7PiwDkAXhlu/O3KbhbBrtZ4O1iSklfC3rM53Na2eapZCiGEEEKIpuHs7MzgwYOZN28e06dPrzEQmZ6eztKlSxk7dqxhRvG1tGnT5pJ11LUZWL2a7OxsTp06xVdffcWtt94KwI4dOxrUp1qt5rHHHuObb77B29sbMzMz7r33XgBOnjxJdnY27777riHP279//1X7c3V1JT09HUVRDK/VxQXs3N3d8fLy4uzZs4wePbpBsddVnYZIDxw4QGZmJl26dMHExAQTExO2bdvG//73P0xMTNDpdAAUFBQwdOhQbG1tWbVq1SWfpPxbz549OXfunGFquYeHBxkZGTXaZGRkYGdnd9nRcABzc3Ps7OxqfDVU54sqp+v1LbdidWxmIfct2MWba09QWqGnT4gz66b15bE+gYzu6Q/Awp3xTR7Xb9GplFfqaethS8/Axtuu7EanUqno29oFgG2nZT9xIYQQQoibweeff05ZWRmRkZFs376d5ORk/vrrLwYPHoy3t/c113df7PHHH+fkyZO89NJLnD59muXLlxt2paptMv9vjo6OODs78+WXXxIbG8vmzZtrDIzW12OPPUZKSgr/+c9/eOihhwz5n5+fH2ZmZnz22WecPXuW1atXM3v27Kv21b9/f86fP8/7779PXFwcc+fO5c8//6zR5o033uCdd97hf//7H6dPn+bo0aMsWrSIjz/+uMHP5WrqlIgPHDiQo0ePEh0dbfjq1q0bo0ePJjo6Go1Gg1arZciQIZiZmbF69eprjpxD1acSjo6OmJtXFUSLiIhg06ZNNdps2LCBiIiIuoTbYG08bDE3UVNQWsnZrKImvXdtVOr0LNgWx+3/+5uDSXnYmJvw9t3t+X5CT3ydqtagjO7lh5lGzaGkPA4m5TZpfMv3JwNwfzffev+CiyrV25htPyOJuBBCCCHEzaBVq1bs37+foKAg7r//foKDg5k8eTK33XYbu3fvxsmp9gNdgYGBrFy5kl9++YUOHTowf/58w/Zn1TlYXanVan788UcOHDhAeHg406dPNxSDawg/Pz8GDRpEbm5ujeJsrq6uLF68mBUrVhAWFsa7777Lhx9+eNW+QkNDmTdvHnPnzqVjx47s27fvkmrxEydO5Ouvv2bRokW0b9+efv36sXjxYgIDAxv8XK5GpTRwc+L+/fvTqVMnPv30U0MSXlxczKpVq2qsVXB1dUWj0bBmzRoyMjLo1asXFhYWbNiwgeeff57nn3/esMY7Pj6e8PBwpk6dyvjx49m8eTPPPPMMa9eurXXVdK1Wi729vaFYXH3dt2AXUQm5fHhfR+7t2jhrnE+kaflg3SkcLE3xd7YmwMWq6k9nKxyszC57zan0Al5ceZjD56r2aO/X2pV37mmPl8OlMwaeW36Ynw+eY0RHLz57qHOjPId/O5aSzx2f7cBMo2bvfwbiaH355yFqJ7+kgi6zN6DTK+x46bZG2QIuv6SCffE57DmbTWxmIU/2D6ZnkLPR7yOEEEII0VRKS0uJj48nMDCwVgOEN5O33nqLBQsWkJyc3NyhXHeu9nNV2zzUqFWfDh48yN69ewEMle2qxcfHExAQgKmpKXPnzmX69OkoikJISAgff/xxjSp1gYGBrF27lunTpzNnzhx8fHz4+uuvm2zrsot18nUgKiGX6OTcRkvE522NY/PJy29Eb29pSoDzP4m5v7M1ybnFzN0SS4VOwdbChFfvCOPerj5XHHV+rE8APx88xx9H0/jP7W3xtL/89H5jWnFhNHxwmLsk4UZgb2lKJ18HDiTmsv10Fg/39Gtwn9rSCqLic9gdl82e+GxiUrVc/LGcuYlaEnEhhBBCiBvEvHnz6N69O87OzuzcuZMPPviAp556qrnDumk1OBHfunWr4fv+/ftfs8T+0KFDGTp06DX77d+/P4cOHWpoeA3WydcRiOdQUl6j9K8oCrvjsgB4pJc/5ZV6ErKLSMwuJl1bSn5JBYfP5RtGvi82KNSNt+5uj7vd1T/dC/e2p2egE3vjc/h2dyIvDW3bKM+lWmmFjl+jq7aEuL97wwrmiX/0beV6IRE/X69EvKC0gqiEHPacrUq+Y1Lz+Xfpg6ALheH+PpNFSl6JkSIXQgghhBDN7cyZM7z55pvk5OTg5+fHc889x8yZM5vs/lOmTOH777+/7LkxY8awYMGCJoulJZB9kK6hk58DACfTCygp12FpZtwy9mcyC8kqLMfCVM0rd4RibvJP/yXlOpJyii8k5kUkZBeTmF1EYZmOx3oHMLKTV63XXk+4JZC98Tks25vEMwNaGf15XGz98QzySyrwsrfglhCXRrvPzaZvaxc+2XianXFZVOr0mNRhO7otpzJ58vuDlFToahwPdLGmV5ATvYKc6RXkjLudBSfStAyb8zepkogLIYQQQtwwPvnkEz755JNmu/+sWbMuWZ9dzRiFtq83kohfg5e9Ba625pwvKONYaj7dA4xb/XtnbNVoePcApxpJOIClmYY2Hra08bBt8H0Ghrrj52RFUk4xvxw6Z6im3hiWR1VNS7+3qw8a2bLMaDr4OOBgZUpecQXRyXl0q+XPYmxmAc8sO0RJhQ5fJ0v6BLsYEm8P+0tnU3g7Vi1dyC2ukH3LhRBCCCGEUbi5ueHmJtvwVqtT1fSbkUqlolP1NmaNMD19V1w2ABHBjbsWV6NWMa53AAALd8Q32nZsyTnF7Lww1f6+bjIt3Zg0ahV9Lsww2F7LbczyiyuYuGQ/BWWV9AhwYtOM/rw7qgN3dfa+bBIOYGdhiq15VfIto+JCCCGEEEIYnyTitdDpov3EjUmnV9hztioR7xPc+FO47+vmg425CXHnixptG6yVB86hKNA72NmwhZownn4XtjHbdibrmm0rdXqe+uEgCdnFeDtYMm9MF8xMavcrXz0qfi5XEnEhhBBCCCGMTRLxWuh8YZ24sRPxmNR8CkorsbUwoZ1X46+LsLUw5f4Lo9QLdyYYvX+9XmHlgXMAPCBF2hrFra2rPrA5ci6P3KLyq7Z998+T/H0mC0tTDV+O7YqLTe33iPS+sA1eal5p/YMVQgghhBBCXJYk4rXQwccBlQpS8krI1BovMdkZWzUa3jPQuU6FtxpiXO8AVKqqqc1nMgqM2vfOuKpK27YWJkS28zBq36KKp70lrd1tUBTYEXvlUfGfD5zj6x3xAHx4X0faednX6T7V+9Gn5BXXP1ghhBBCCCHEZUkiXgs25ia0dqsqmHbIiKPiuy6spe7dyOvDL+bnbMXgUHcAFu1KMGrfy/dXjYbf1ckbC9PGq8p+s+t7YXr6ldaJH0rKZeaqowA8MyCE4R0863yP6qnpMiIuhBBCCCGE8UkiXkvGXideXqknKiEHwFCAq6lMuCUQgF8Onrvm9ObayisuZ11MOoBh+rtoHH1bX0jEz5xHUWoW3UvPL+Xx7w5QXqlnSJg70wa1rtc9DCPiskZcCCGEEKJZKIrC5MmTcXJyQqVSER0d3dwhCSOSRLyWqvcTN1bl9OjkPEor9Dhbm9Ha3cYofdZWj0An2nnZUVqh54eoJKP0+Vt0KuWVekI97Qj3vvn2AWxKPQKdMDdRk6Et43RGoeF4aYWOx7/bT2ZBGa3dbfj4gU6o67l9nLdharok4kIIIYQQzeGvv/5i8eLF/P7776SlpaHVahkxYgReXl6oVCp+/fXX5g5RNIAk4rVUPSJ+5FweOiNs/VW9f3hEsDMqVdPuta1SqRjfp2pU/NtdiVTo9A3u86cLe4c/0M2nyZ/PzcbCVEPPoKrlDNXT0xVFYeYvRzl8Lh8HK1O+HtsdG/P67/9dnYina0upNMLPhxBCCCGEqJu4uDg8PT3p3bs3Hh4eFBUV0bFjR+bOndvcoQkjqP879ZtMa3dbrMw0FJXriM0spI2HbYP6231h//DeTbBt2eXc0dGTd/48Sbq2lD+PpXNnR69693UsJZ/jaVrMNGpGdvI2YpTiSvq2cmH76fNsP3OeSX2D+Orvs6w6lIJGrWLew13wc27Y1nFutuaYalRU6BQyCsoMibkQQgghxPVOURRKKpt+1p+liWWtB6zGjRvHkiVLgKpBNH9/fxISEhg2bFhjhiiakCTitaRRq+jgY8+eszlEJ+c2KBEvLq/kUHIuAH1Cmq5Q28XMTTQ80sufTzaeZuGO+AYl4sv3V42GD2nnjqO1mbFCFFfRr7Urb649wd74HP46ls67f54E4NU7wuhthJoDarUKD3sLknNKSM0rkURcCCGEEDeMksoSei7r2eT33fvwXqxMazdYMmfOHIKDg/nyyy+JiopCo5FCyDcamZpeB518HQHYF5/boH72J+RSoVPwdrDEz6lhI5cNMbqXH2YaNdHJeRxMqt9zKq3Q8euhFECKtDWlEDcbPO0tKK/U8+TSA+gVeLC7L2Mj/I12D28p2CaEEEII0Szs7e2xtbVFo9Hg4eGBq6trc4ckjExGxOvgtjauLNgWx1/H0pg1sh3W9VyDuzOu+daHX8zFxpyRnbxYceAc3+yIp8vDjnXuY11MOtrSSrwdLJu8+vvNTKVS0beVKz/tT0avQDd/R2aNDDfqz5OXFGwTQgghxA3I0sSSvQ/vbZb7ClFNEvE66BHoRJCLNWezilhzOJUHe/jVq5/q9eHNNS39Yo/1CWTFgXP8dSydlHpMQV5xYe/wUV190NSzQreon0Fh7vy0Pxkvewvmj+mKmYlxJ7j4SCIuhBBCiBuQSqWq9RRxIRqLTE2vA5VKxQPdq6Zf/3ChSnhd5RdXcCwlH4CIoOYfQQ7zsqN3sDM6vcK3uxPqdG1yTjE7LlR/v6+rTyNEJ65mUKgbC8Z05Zcn++Bqa270/qtHxFMlERdCCCGEEMKoJBGvo1FdfTDVqDicnMeJNG2dr98bn41egSBXazzsLRohwrqr3srsh71J5BSV1/q6lQeqRsP7hDjj24xr3W9WKpWKoeEejfZz5O0oa8SFEEIIIVqKwsJCoqOjiY6OBiA+Pp7o6GiSkpKaNzBRL5KI15GLjTmDw9wB+HFf3X/odxm2LWv+aenVBrR1I8DZCm1pJbe8t5nZvx+/5iioTq8YEnEp0nZjunhEXFGUZo5GCCGEEOLmtn//fjp37kznzp0BmDFjBp07d+bVV19t5shEfUgiXg8Pdq9aG77qUAqlFbo6XbvrQqG2Ps20f/jlqNUq5jzYmVBPO4rLdXyzI56+729hxvJoTqUXXPaaXXFZpOSVYGdhQmQ7jyaOWDSF6noBReU68ksqmjkaIYQQQoiby7Rp00hISDA87t+/P4qiXPK1ePHiZotR1F+DEvF3330XlUrFtGnTDMdKS0uZOnUqzs7O2NjYMGrUKDIyMmpcl5SUxPDhw7GyssLNzY0XXniBysrKGm22bt1Kly5dMDc3JyQkpEX9gN0S4oKPoyXa0kr+OJpW6+vOF5RxOqMQgF5BLWdEHKCjrwN/PHMLS8b3ICLImUq9wi8HU4j8dDvjF0exLz6nxqjoTxfWyN/V2RsLU9nX8EZkYarB+cK+8FKwTQghhBBCCOOpdyIeFRXFF198QYcOHWocnz59OmvWrGHFihVs27aN1NRU7rnnHsN5nU7H8OHDKS8vZ9euXSxZsoTFixfXmFIRHx/P8OHDue2224iOjmbatGlMnDiRdevW1Tdco1KrVTxwYTr2j/tqX7Rt99mqaelhnnY4XkhwWhKVSkW/1q78MLkXv03tw+3tPVCpYPPJTO7/Yjf3zN/Fuph0corKWR9T9eGKTEu/sck6cSGEEEIIIYyvXol4YWEho0eP5quvvsLR8Z+9p/Pz8/nmm2/4+OOPGTBgAF27dmXRokXs2rWLPXv2ALB+/XqOHz/O999/T6dOnRg2bBizZ89m7ty5lJdXFQpbsGABgYGBfPTRR4SGhvLUU09x77338sknn1wxprKyMrRabY2vxnRfN1/UKtiXkENsZmGtrtl1ocJ4S1offiUdfR2YN7orm5/rz8M9/TAzUXMoKY/HvztA/w+2UK7TE+ZpR7i3fXOHKhqRl71UThdCCCGEEMLY6pWIT506leHDhzNo0KAaxw8cOEBFRUWN423btsXPz4/du3cDsHv3btq3b4+7u7uhTWRkJFqtlpiYGEObf/cdGRlp6ONy3nnnHezt7Q1fvr6NO1LrYW/BgLZuAPwUVbuibbsM+4e3nPXh1xLoYs3bd7dnx0u38WT/YGwtTNCWVi0jqN7KTdy4DCPikogLIYQQQghhNHVOxH/88UcOHjzIO++8c8m59PR0zMzMcHBwqHHc3d2d9PR0Q5uLk/Dq89XnrtZGq9VSUnL5hGDmzJnk5+cbvpKT67fPd11UF237+WAKZZVXL9qWnFNMUk4xGrWK7oFOjR6bsbnZWvDi0LbsnjmQV4aHMrlvkCTiN4F/KqeXNnMkQgghhBD1JzvACGMyxs+TSV0aJycn8+yzz7JhwwYsLFrGHtjVzM3NMTc3b9J79m/jirudORnaMjYcz+CODl5XbFu9Pryjjz025nV62VsUG3MTJt4a1NxhiCZSXTn9nIyICyGEEOI6ZGpqCkBxcTGWlpbNHI24URQXFwP//HzVR50ywgMHDpCZmUmXLl0Mx3Q6Hdu3b+fzzz9n3bp1lJeXk5eXV2NUPCMjAw+Pqi2uPDw82LdvX41+q6uqX9zm35XWMzIysLOza1G/QCYaNfd38+WzzbH8uC/5qon4P+vDr59p6UJ4O8gacSGEEEJcvzQaDQ4ODmRmZgJgZWWFSqVq5qjE9UpRFIqLi8nMzMTBwQGNpv67R9UpER84cCBHjx6tceyxxx6jbdu2vPTSS/j6+mJqasqmTZsYNWoUAKdOnSIpKYmIiAgAIiIieOutt8jMzMTNrWqN9YYNG7CzsyMsLMzQ5o8//qhxnw0bNhj6aEnu7+bL51ti2RGbRVJ2MX7OVpe0URTFsD68d0jLL9QmRLXqNeLnC8oordDJVnVCCCGEuO5UD/ZVJ+NCNJSDg4Ph56q+6pSI29raEh4eXuOYtbU1zs7OhuMTJkxgxowZODk5YWdnx9NPP01ERAS9evUCYMiQIYSFhfHII4/w/vvvk56eziuvvMLUqVMNU8unTJnC559/zosvvsj48ePZvHkzy5cvZ+3atQ16so3B18mKW0Jc+PtMFj/tT+KFyLaXtIk7X0RmQRlmJmq6+DlephchWiZHK1MsTNWUVuhJzy8lwMW6uUMSQgghhKgTlUqFp6cnbm5uVFRUNHc44jpnamraoJHwakZfrPzJJ5+gVqsZNWoUZWVlREZGMm/ePMN5jUbD77//zhNPPEFERATW1tY8+uijzJo1y9AmMDCQtWvXMn36dObMmYOPjw9ff/01kZGRxg7XKB7q4cffZ7JYsf8c0we1xkRTswberriqaend/B1lRFFcV1QqFd4OlsSdLyIlr0QScSGEEEJctzQajVESKCGMocGJ+NatW2s8trCwYO7cucydO/eK1/j7+18y9fzf+vfvz6FDhxoaXpMYFOqOs7UZmQVlbD6ZyZB2Nacp7Iq9/rYtE6Ka10WJuBBCCCGEEKLh6rWPuKjJzETNvV19APgxqua2aXq9YqiYHhEs68PF9cenei/xXEnEhRBCCCGEMAZJxI2kek/tracyScv/J2E5nqYlv6QCG3MTOnjbN1d4QtSbl71UThdCCCGEEMKYJBE3kiBXG3oGOqFXYHnUOcPx6vXhPQKdLlk7LsT1oLpyukxNF0IIIYQQwjgkMzSih3r4AbB8fzI6vQLwz7ZlMi1dXKdkL3EhhBBCCCGMSxJxIxoa7oG9pSkpeSX8feY8FTo9++JzAOgdLIXaxPXJy5CIl6K/8AGTEEIIIYQQov4kETciC1MNd3f2BuDHfckcOZdHcbkORytT2nrYNnN0QtSPh70FahWU6/RkFZU1dzhCCCGEEEJc9yQRN7Lq6ekbT2TwW3QqUFUtXa1WNWdYQtSbqUaNu50FIJXThRBCCCGEMAZJxI2sjYctnf0cqNQrfLcnEZBp6eL6V71OXAq2CSGEEEII0XCSiDeCh7pXjYorF5bTSqE2cb3zkoJtQgghhBBCGI0k4o3gjo6e2JibAOBhZ0Ggi3UzRyREwxi2MJOp6UIIIYQQQjSYJOKNwMrMhJGdvADoE+KCSiXrw8X1zcswNb20mSMRQgghhBDi+mfS3AHcqF4a1hZPewvu7+bb3KEI0WA+skZcCCGEEEIIo5FEvJHYWZjy1IBWzR2GEEYha8RvLCXlOvSKgrW5/BcghBBCCNEcZGq6EOKavByqti/LL6mgsKyymaMRDbHlZCbd39rIsDl/oy2taO5whBBCCCFuSpKICyGuydbCFDuLqtFTGRW/fi3ZlcCEJVEUllWSlFPMpxvONHdIQgghhBA3JUnEhRC14u1oBUjl9OuRTq/w+uoYXlsdg16BW1u5ALBkdwIn07XNHJ0QQgghxM1HEnEhRK14X5ieLgXbri+FZZVM+nY/i3clAPB/w9ry7fgeDG3ngU6v8OpvMSiK0rxBCiGEEELcZCQRF0LUirdUTr/upOWXcN+C3Ww+mYm5iZr5o7swpV8wKpWK/44Iw8JUzb74HH6LTm3uUIUQQgghbiqSiAshakUqp19fjqXkc9fcnZxI0+JiY8aPk3sxrL2n4by3gyVP3RYCwFt/nKBACrcJIYQQQjSZOiXi8+fPp0OHDtjZ2WFnZ0dERAR//vknAAkJCahUqst+rVixwtDH5c7/+OOPNe6zdetWunTpgrm5OSEhISxevLjhz1QI0SDejhdGxGWNeIu34XgG9y3YTYa2jFZuNqx6sg+d/RwvaTepbxABzlacLyhjzkYp3CaEEEII0VTqlIj7+Pjw7rvvcuDAAfbv38+AAQMYOXIkMTEx+Pr6kpaWVuPrjTfewMbGhmHDhtXoZ9GiRTXa3XXXXYZz8fHxDB8+nNtuu43o6GimTZvGxIkTWbdunVGesBCifmREvOVTFIVvdsQz+bv9lFTouLWVCz8/2RtfJ6vLtjc30fD6ne0AWLQrgdMZBU0ZrhBCCCHETcukLo1HjBhR4/Fbb73F/Pnz2bNnD+3atcPDw6PG+VWrVnH//fdjY2NT47iDg8MlbastWLCAwMBAPvroIwBCQ0PZsWMHn3zyCZGRkVeMraysjLKyMsNjrVYqAQthTD4XEvF0bSkVOj2mGlnZ0pJU6vS8seY43+1JBOChHn7MGtnumn9P/du4MSTMnfXHM3j1t2P8MKkXKpWqKUIWQgghhLhp1fudtE6n48cff6SoqIiIiIhLzh84cIDo6GgmTJhwybmpU6fi4uJCjx49WLhwYY2Kvbt372bQoEE12kdGRrJ79+6rxvPOO+9gb29v+PL19a3nMxNCXI6LjTlmGjV6BTK0pc0djrigtELHb9EpPPDlHr7bk4hKBf+5vS1v3x1e6w9L/ntHGOYmavaczWHNkbRGjlgIIYQQQtRpRBzg6NGjREREUFpaio2NDatWrSIsLOySdt988w2hoaH07t27xvFZs2YxYMAArKysWL9+PU8++SSFhYU888wzAKSnp+Pu7l7jGnd3d7RaLSUlJVhaWl42rpkzZzJjxgzDY61WK8m4EEakVqvwdLAgMbuYlNwSfBwvP91ZND5FUdifmMvPB86x9kgaBWWVAFiYqvn0gU4MDfe8Rg81+TpZMfW2ED7ecJq31h5nQFs3bMzr/N+DEEIIIYSopTq/02rTpg3R0dHk5+ezcuVKHn30UbZt21YjGS8pKWHZsmX897//veT6i4917tyZoqIiPvjgA0MiXl/m5uaYm5s3qA8hxNV52VuSmF1Mar6sE28OyTnF/HIwhV8OnSMxu9hw3NvBklFdvLmvm+8V14Nfy+S+Qfx8sKrfzzadYebtocYKWwghhBBC/EudE3EzMzNCQqq2vOnatStRUVHMmTOHL774wtBm5cqVFBcXM3bs2Gv217NnT2bPnk1ZWRnm5uZ4eHiQkZFRo01GRgZ2dnZXHA0XQjQNqZze9ArLKvnjSBo/HzzH3vgcw3FrMw3D2nsyqosPPQOdUKsbtq7bwlTDayPCGL94P9/siOferj60crdtaPhCCCGEEOIyGjz3UK/X1yiSBlXT0u+8805cXV2veX10dDSOjo6G0eyIiAj++OOPGm02bNhw2XXoQoimVV05PSVP1og3tuLySt778yQ/7U+mtEIPgEoFfYJduKeLN0PDPbAyM+708QFt3RkU6sbGE5m8tjqGpRN7SuE2IYQQQohGUKd3cTNnzmTYsGH4+flRUFDAsmXL2Lp1a42txWJjY9m+ffslyTTAmjVryMjIoFevXlhYWLBhwwbefvttnn/+eUObKVOm8Pnnn/Piiy8yfvx4Nm/ezPLly1m7dm0DnqYQwhh8DIm4jIg3phNpWp5adpC480UABLlaM6qLD3d39jZ8GNJYXhvRjr/PZLErLpu1R9O4o4NXo95PCCGEEOJmVKdEPDMzk7Fjx5KWloa9vT0dOnRg3bp1DB482NBm4cKF+Pj4MGTIkEuuNzU1Ze7cuUyfPh1FUQgJCeHjjz9m0qRJhjaBgYGsXbuW6dOnM2fOHHx8fPj666+vunWZEKJpyF7ijUtRFL7fk8jstScor9TjbmfOB/d25NZWLk02Mu3rZMUT/YP5dOMZ3vz9BLe1ccNaCrcJqpZJaEsqGv3DICGEEKKuFL2eAm0uhXlZaExMsHN0w8LSGpW65W63q1Iu3jvsBqLVarG3tyc/Px87O7vmDkeIG0J8VhG3fbgVS1MNx2dFyrRlI8orLueln4+wLqaqRsaAtm58cG8HnG2avghlaYWOwZ9sIzmnhCn9gvm/YW2bPAbRsiiKwt3zdnE8Vcv66X0JcLFu7pCEEELcBBS9nmM71lCUHI1SnIu6LB+TsjxMK7RYVGqx0hVgrRRiqxRhotLXuLZMMUWrsqFIbUuxxo4yUzsqzezRmTugWDqgtnLCxieckC79Mbcw3m5Atc1DZZhDCFFrnvYWAJRU6MgrrsDR2qyZI7oxRCXk8OwPh0jNL8VUo+L/hoUyvk9As33QYWGq4bU72jHx2/18s+Ms93XzIdjVplliuRGdyy3mr2Pp/HksndPpBXz2cGf6t3Fr7rCuasupTKKT8wDYfTZbEnEhhBCN7uT+TejXvUr7imPXbnzhLVOpYooGPaYqHeaqClzJxVWfC3qgAij+13XHoXidOUcs21Pscwsu7YcQFN4LtUZj5GdzKUnEhRC1ZmGqwcXGnKzCMlLySiQRbyCdXmHellg+2XgavQIBzlZ89lAX2vvYN3doDApzZ0BbNzafzGTSt/t5uIcfke086r092s0uIauIP4+l89exNA6fy69x7pVfj7FxRj8sTBv/P/36mrclzvD9sZT8q7QUQgghGibpdDRZv71Cl6K/gark+rhtbyosnNFbOKCydERt5YiZjTNmtk5Y2btgbe+CrYMLFlY2KHo9RUVaCnLPU5R3nhJtFuUF2VQU5qAvzkFVkou6LA/Tslz8imNwVuXToXQ/xO6H2E/JXWVLvE0XKvz74dP1dryDGmdLV0nEhRB14u1gYUjEw72bP2G8XmVoS5n2YzS7z2YDcHdnb2bfFY5NC1qP/dqIMPYn5HD2fBFvrj3Bm2tP0N7bnqHhHkS28yDETUbJryY2s5A/j6bx57F0jqdpDcfVKuge4MSwcA8WbDvLudwSvtkRz9TbQpox2iuLSshhf2Ku4fGxVO1VWgshhBD1k5WeRNyK/9I1azV+Kj06RcUBx2H43/smXXyCa92PSq3G2tYBa1sH8Gt11baKXk/8iSgyDq/HInkHrYqjcVQV4Fi4DWK2QcwsUlVunHPoDn4RuLbugW/rzpiYNnwwquW84xNCXBe8HS05fC5f9hJvgM0nM3h+xRFyisqxMtMwe2Q4o7r6NHdYl/B3tmbjjH78cTSNv2LS2Refw9GUfI6m5PPBulOEuNkw7EJS3s7LTmoGANmFZXy7O5E/j6VxOqPQcFyjVhER5Myw9h4MCfPA1bZq7b+DlRnTfopm7pZY7uvqg5udRXOFfkXztsQC0CfEmZ2x2ZxI01Kh02OqabkFcIQQQlw/CrW5HF3xJh2TvqOnqgxUEG3ZC/sRb9IjrHuj3lulVhPYrieB7XoCUFFexsno7eQe24B9+i5Cyo7jRSZeuWshdy0chhLFjDjTIPIc2qHy7lzv5FyKtQkh6uSttcf56u94Jt4SyCt3hDV3ONcVbWkFH607xZLdiQCEedrx+cOdCbpO1l9nFZax8XgGf8WkszM2iwrdP/99+DhaMrSdB6N7+RN4k64f/uNoGv/99RjZReUAmGpU9Alx4fZwTwaHuV92KYder3DP/F1EJ+dxb1cfPryvY1OHfVUn0rQMm/M3ahVseq4/d36+g4LSSv589lZCPeX/ViGEEPVXUV7GwVWfEHJiHs5ULXs6bdKaigFv0K737c0cXZXiwnxiozZQfGoztjlH8C+LxUZ16WBUiWJGkmkQeQ5hFDmGMnDMS1KsTQhhXF6yl3idKYrCqkMpvP3HSbIKywAY1zuAmbe3xdyk5a4L/jcXG3Me7OHHgz380JZWsOVkJn8dS2frqfOcyy3h6x3xrDhwjl+e7H1TFXfLKSrnv78dY+2RNADauNvyeL8gBoa6Y29petVr1WoVr40I4+55u1h54BxjI/zp4OPQBFHXzoJtVWvDb2/vSaCLNe287NhztmpmhCTiQggh6qIgP4esc2fQZsRTkh6Lz5nv6alU/d95TuVJZo+X6Bz5aIvacszKxp4Ot90Lt90LgF6nI/nsMTJO7qHy3CFsc48ZkvM2lSch6yTalNqNc0siLoSoE29JxOvkRJqWV387RlRC1RrbIFdr3rizHbe2cm3myBrGzsKUkZ28GdnJm5JyHdtOn+fzLWc4lqLlsUVRrHqyd7NsvdbU/jqWxiu/HiOrsByNWsWT/YN5ekArzExq/yais58jd3f2ZtWhFGatOc6KKREtYpp/UnYxaw6nAjClX9XavHAve/aczSEmJR+6+TZneEIIIVqQstJicjNTyMtIpDAznsrsJFTac5gXpWJXlo6LPhM7irH913XZ2BMbOpUud0/Dx6zlv29QazT4tuqIb6t/ZrDpdTqS4o6SeWovlecOoUqLBvZesy9JxIUQdVI9Ip4qifhVaUsr+Hj9ab7bk4hOr2BpquGZga2YcEtgnZK064GlmYah4R50D3Dk7nm7SMopZvJ3B1g6sWeLrgTeELlF5by2OobVFxLV1u42fHhfx3qPZr80tC1/HUtnf2Iua46kcWdHLyNGWz9f/h2HXoG+rV0NhRmrK/pLwTYhhLg5lJeVkngiiuKcFMrzMtBpM6AoE9OS81iUZ2NbmYODPg87ivAAPK7RXx42ZGvc0Jp7UOremfZ3v0BPO8emeCqNRq3R4Ne6E36tOwFVS6SZfe2CxpKICyHqxMexKhHPKiyntEJ3wyZa9aUoCr8cTOGdP/+Zhj68vScvDw81fIhxo3K2MWfhuO7cM28nBxJzeX7FYf73YGfU6uYf3TWmdTHpvLzqGFmFZahVVaPFzw5q1aBlBh72FjzRP5iPN5zm3T9OMCTMvVl/t84XlLF8/zkAnuz/T6Xadl5VbyyOp2rR6RU0N9jfrRBCiH+knI1B+W4UrS5MH7+WckVDjsqJHFN3ii09qbD1Ru3gi6VLAPYegbj4BONg64BD44Z93ZBEXAhRJ/aWpliZaSgu15GaV3LdFBoDKCit4ERaAZ39HBql4vPx1Kpp6NVbPQW5WjPrznBuaeVi9Hu1VCFuNnzxSDfGLtzL70fSCHC25vnINs0dllHkFZfz+uoYfo2uGgUPcasaBe/k62CU/if3DeLHfUmk5pfy5fazPDPw6luuNKaFO+Mpr9TT2c+BnoFOhuOBLtaG3/+z5wtp5f7vSYZCCCFuBLGHd+K46iGcyUeLFRkaL4rNnCgzd0Fn5YrKxg1Tew8sHDyxcfbEwc0XOwdnPNTqa46KiyqSiAsh6kSlUuHtYMmZzEJSrrNE/MmlB/n7TBbeDpZMvDWQB7r7YmXW8H8G84rL+XTjGb7dnYBeASuzqmno4/vceNPQayMi2Jl37unA8ysO8/mWWPycrLi/+/W9nnjD8Qz+s+oo5wuqRsEn9w1m2qBWRh21tjDV8H+3h/LMD4eYvzWO+7v54mHf9NuZaUsr+P5CZf8n+gXXWK+uUato52VHVEIuR1PyJREXQogb0LEdqwnYMBkbVQlxmiDsJ/1GKw+/5g7rhnPzvUMUQjTY9bhO/EBiLn+fyQKqCs29seY4vd/dzMfrTxmmkNdFWaWOdTHpPLn0AD3e3sTiXVVJ+PAOnmx6rh9T+gXflEl4tXu7+vDMgBAA/rPqKDtjs5o5ovopKdcx85cjTPp2P+cLygh2tebnJ3rzf8PaNsrU8REdPOnm70hJhY73/jpp9P5r4/s9iRSUVdLKzYZBoe6XnK+enn4sRdaJCyHEjebgn4toveExbFQlxJh1wPXpjbhIEt4oZERcCFFn3hfWiafkXj+J+PytsQDc1cmL7oFOfLX9LAnZxfxvcyxfbD/Lfd18mHRrEP7OV94DW69X2J+Yy6/RKaw9kkZ+SYXhXKinHS/fHnpTTUO/lumDW5OYU8xv0alM+f4AvzzR+7oaQT2dUcBTyw5yOqMQlQom3hLIc0PaNOrabZVKxasjwrjz852sOpTCIxH+dPFruiI2pRU6Fu5IAKrWvl9ufX977+qCbflNFpcQQojGt3f5B3SPeQu1SuGgdV/CnvoRC8srvy8SDSOJuBCizv7Zwqy0mSOpnVPpBWw8kYlKBU8PbEWwqw0PdvdjfUw6C7bFcfhcPt/vSWLZ3iSGhXvyeL+gGtWvz2QU8Gt0Cr8eSq2xbZu7nTkjO3lzVydvQj1tW8SWUy2JSqXivVEdSM0rISohl8cWR7HqyT642rbs7UkUReGnqGReXxNDaYUeFxtzPn2gU5N9yNLBx4F7u/qw8sA5Zq05zi9P9G6ygncrD5wjq7AMbwdL7ux0+crt1RXUj6dq0euVG64YnxBC3GwUvZ49i14kIvkrUMFe57vo9sQ3aEwkVWxM8uoKIersn0S8uJkjqZ3q0fBh4R4EX1jTrlGrGNbek6HhHuyNz+GLbXFsOXWetUfTWHs0jYggZ3oHO/NXTDoxF23VZGNuwrBwD+7u7E3PIGepGn0NFqYavnikG/fM20lCdjETv93Pj5N6YWnWMqvtF5RWMPOXo/x+pKpC7K2tXPj4/k5N/uHBi5Ft+PNoGtHJeaw+nMpdnb0b/Z6VOj1fbI8DYNKtgVcsaBjsao2FqZrCskoSsouuqzoRQgghatJVVrJ//gQisn8FYLfvJHo99j4q9c27vK6pSCIuhKizf9aIt/wR8aTsYtZcSKqe7B9yyXmVSkWvIGd6BTlzMl3Ll9vPsjo6ld1ns9l9NhsAE7WK/m3cuKuzF4NCm3dbqeuRk7UZix7rwd3zdnI4OY8Zy6OZ+3CXOo+k6vQKReWVFJVVUlhaSWFZJUVlOgrLqr+vpKRCRzsvO3oFOde5Mv6Rc3k8tewQSTnFaNQqnh/Shsf7BjXLiK+bnQVP3hbCB+tO8e6fJxnSzt0ohQWvZu3RNJJzSnCyNuOB7ldeD2iiURPqacehpDyOpWolERdCiOtUWWkxMZ8/QM/C7egVFVFhM4l44KXmDuumIYm4EKLOqteIp+WXtPipqV9sj0OnV7i1lYthSu2VtPWw4+P7O/H8kDYs2hnP2fNF9G/rxvD2njhZmzVRxDemQBdrvnykG2O+3sufx9J576+TzLw9FKhae59VWEZqfilpeSUX/VlCal4p6fmlaEsrKC7X1fp+thYmDGzrxpB2HvRr7Yq1+ZX/u1MUhW92xPPeXyep0Cl4O1jyv4c609W/6dZmX86EWwL5YV8S53JLWLDtLDMGt260eymKwvytVaPh43oHXHPGQriXfVUinpLPnR0vP4VdCCFEy1WQn0PS3LvoUn6YcsWEoz0/pOftjzV3WDcVScSFEHXmbmuORq2iQqdwvrAMd7um32KpNjILSllx4Bxw+dHwK/FysOTl4WGNFdZNq0egE+/f24FpP0Xzxfaz7InPIbuwjAxtKRU6pdb9mKhV2FiYYG1mgo25SdX35ibYmGtQq1TsOZtNVmE5v0an8mt0KmYmam4NcWFIO3cGhrrjYvPPNPOconKeX3GYzSczARjazoP3RnXA3srU6M+/rixMNfzn9lCeXHqQL7bF8UB3X8OyEGPbeuo8J9MLsDbT8GhEwDXbGwq2pUjBNiGEuN5kpSeT99VI2uniKFIsiB/8FV1vubO5w7rp1CkRnz9/PvPnzychIQGAdu3a8eqrrzJs2DAA+vfvz7Zt22pc8/jjj7NgwQLD46SkJJ544gm2bNmCjY0Njz76KO+88w4mFxUD2Lp1KzNmzCAmJgZfX19eeeUVxo0bV8+nKIQwNhONGg87C1LySjiXW9JiE/FvdsRTXqmns58DvYKcmjscAdzV2ZuknGI+3nCaw8l5huNqFbjZWuDpYIGXvSVeDhZ4XvjTw94SRytTbMyrEm5zE/VVC+Pp9AqHknJZfzyDdTHpJGYXs+lkJptOZqJSHaWbvyOR7TzwcbTi9dUxpGtLMTNR89/hoYzp5d+iiu4NC/egR6AT++JzePfPk3z2UOdGuU/1aPjDPf1q9SFEO287oCoRVxSlRb1mQghxo4je9COVh1cS8OBHuHj5G6XP4sJ8Cr4cTog+kWzsyb1nGeEdbzFK36Ju6pSI+/j48O6779KqVSsURWHJkiWMHDmSQ4cO0a5dOwAmTZrErFmzDNdYWVkZvtfpdAwfPhwPDw927dpFWloaY8eOxdTUlLfffhuA+Ph4hg8fzpQpU1i6dCmbNm1i4sSJeHp6EhkZaYznLIQwAi+HqkQ8Na+k2afwXk5+SQVL9yQBVaPhkii0HE8PCKG1uy3lOj1e9hZ4OljiZmte5zXdV6JRq+gW4ES3ACdmDmvL6YxC1seks/54BkdT8olKyCUqIdfQPsjVms8e6mzYH7slUalUvHpHGCM+38Gaw6lsP30eE7UKjVpV9adGhYlabXisVqkw0aiwMTehq78jEcHOdPFzvGpdg/0JOexLyMFUo2LCLUG1iquVmy1mGjXa0kqSc0rwc7a69kVCCCFq7XxqAiHbp2GjKuHUwvuxfX4L5hYN+7dW0es5/uV4uukTycKB0jFrCQkJN1LEoq7qlIiPGDGixuO33nqL+fPns2fPHkMibmVlhYeHx2WvX79+PcePH2fjxo24u7vTqVMnZs+ezUsvvcTrr7+OmZkZCxYsIDAwkI8++giA0NBQduzYwSeffCKJuBAtiLeDJVHk1tjOqyX5bncChWWVtHa3YWBbt+YOR1xEpVIxNPzy/080xr3aeNjSxsOWpwe2IiWvhA0XkvKohBxGdvLmjTvbXXUNeXML97ZnbC9/luxOrLF3/bXsisvms82xmJmo6eLnQO9gFyKCneno44CZyT8felSPho/q4oOHfe1mt5iZqGnracuRc/kcS82XRFwIIYwsadmzdFVVvcdqU3mSfV9MovvT3zWomvm+Fe/TU7uRSkVN5rAvCJMkvFnV+52HTqdjxYoVFBUVERERYTi+dOlSvv/+ezw8PBgxYgT//e9/DaPiu3fvpn379ri7uxvaR0ZG8sQTTxATE0Pnzp3ZvXs3gwYNqnGvyMhIpk2bdtV4ysrKKCsrMzzWarVXaS2EaKh/Kqe3vES8pFzHwp0JQNVoeEsuJiealreDJeP6BDKuT+B1NaX69TvbMfHWIMoq9ej0CpV6PXo9VOqrHyvoL/yp0ytkaEvZc6Hyf4a2jD1nc9hzNgc2gKWphm4BVaPlvo5WF6bsw+S+tRsNr9bOy54j5/I5mpLP7e09G+mZCyHEzefIlpV0LdyKTlERFfw0PeI+o0fu7+xd+RE973+hXn2e3L+JzsffBxXsbz2dXr2GGjlqUVd1TsSPHj1KREQEpaWl2NjYsGrVKsLCqooaPfzww/j7++Pl5cWRI0d46aWXOHXqFL/88gsA6enpNZJwwPA4PT39qm20Wi0lJSVYWl6+UM0777zDG2+8UdenI4Sop+rK6Sm5LS8R/ykqiZyicnydLLmjgyQI4vKulyQcqmL1darbqPODPfxQFIWzWUXsjsuu+jqbTU5ROX+fyeLvM1mGtreHe9Z5G7Lwi9aJCyGEMI7S4kKct/8HgCiPB+g1dja7l+iJiP+czjHvcHJvR9r2HFKnPnMyU3D8fRJmKh0HbfrS86FXGiN0UUd1TsTbtGlDdHQ0+fn5rFy5kkcffZRt27YRFhbG5MmTDe3at2+Pp6cnAwcOJC4ujuDgYKMG/m8zZ85kxowZhsdarRZfX99GvacQN7PqEfGWNjW9Qqfnq7/jAZjcNxgTI607FuJ6pFKpCHa1IdjVhjG9/NHrFU5nFrArtiop33M2m0qdwlMDar+rQLWLK6dfT7MLhBCiJTu09GUilAwycKb9mPcA6PXIbA5+fJQuhdtw+XMSmT5bcfMOrFV/uspKUr4ZTXuySVJ703rytw2a3i6Mp86JuJmZGSEhVf9hd+3alaioKObMmcMXX3xxSduePXsCEBsbS3BwMB4eHuzbt69Gm4yMDADDunIPDw/DsYvb2NnZXXE0HMDc3Bxzc/MrnhdCGJdPC03Ef4tOJSWvBBcbc+7r6tPc4QjRoqjVKtp62NHWw47xtwQaprmbm1x93/DLae1ui4laRW5xBan5pY22tZoQQtwsEk8coOu570AFqb1n0dnWAQCVWk2bx78l/pN+BOoTOL3ofuye24qFpfU1+9y36Hkiyg5RrJij3PcdNnYtr8DuzarBH4fo9foaa7MvFh0dDYCnZ9XU0IiICI4ePUpmZqahzYYNG7CzszNMb4+IiGDTpk01+tmwYUONdehCiOZXPSJeUFqJtrT2BaQak16vMH9rLAATbgm8aqVoIURVhfn6JOFQtc95a3dbQKanCyFuTKUlRZQWF6KrrGz0eyl6PUWrnsVMpSPaKoJOgx6ucd7a1gGzMcvIx5rWlac58sVEFL3+qn1Gb/yBiJRFABzv/ib+oV0bLX5Rd3UaEZ85cybDhg3Dz8+PgoICli1bxtatW1m3bh1xcXEsW7aM22+/HWdnZ44cOcL06dPp27cvHTp0AGDIkCGEhYXxyCOP8P7775Oens4rr7zC1KlTDaPZU6ZM4fPPP+fFF19k/PjxbN68meXLl7N27VrjP3shRL1Zm5vgYGVKXnEFqXkl2Hlce+/hxrb+eDpx54uwtTBhTC+/5g5HiBteuLcdx9O0HEvJJ7Jd01TCF0KIprD/9y/pFPUSJqqqZLdSUVOBCRUqU8oxpRITKlWmVKpMqFSZUWzqgPmgV2jTbUC97hf12+f0KD9KsWKO+wNzLjt93DuoHUdv+5ywzePpkfcHe1d8QM8HXrpsfylnTxC0o2rZ7l7Xe+l5x+TLthPNp04j4pmZmYwdO5Y2bdowcOBAoqKiWLduHYMHD8bMzIyNGzcyZMgQ2rZty3PPPceoUaNYs2aN4XqNRsPvv/+ORqMhIiKCMWPGMHbs2Br7jgcGBrJ27Vo2bNhAx44d+eijj/j6669l6zIhWiAv+8apnH4oKZeP15/iVHpBra9RFIV5F7ZhGhvhj61F838wIMSNLvyideLiUun5pZRXXn3ESgjRMpnGrDQk4QAmKj2WqnLsKMKFPDzIwkdJI0CfTIgujg6lB/Bb8wDRG5bV+V6559Nodfh9AI60egJP/zZXbNu+3z1EBT8NQJfj73F8z1+XtCktLqR06cPYUcxJk1A6T5xb55hE46vTiPg333xzxXO+vr5s27btmn34+/vzxx9/XLVN//79OXToUF1CE0I0A29HS46naY1WOf1UegEfrj/FhuNVdSL+tzmWoe08eHpgCO287K967c7YbI6cy8fCVM1jfWpXwEQI0TDVifjRFK0UbPuXqIQcRn+1Fw97C76b0AN/52uv5RRCtAyKXo9/SQwAMYOX4d2mK5XlZZSXl6KrKKWyvJTK8jJ0FWVUVpSiryhH2fsFHUv20n7Hk+zNPUfP+1+s9f3OLJ1BDwqIVwfQ9f7/XLN9zzFvcOCTo3Qt2IL7X5PJ8NmCu0+wIfYjX06ih+4sOdjhOG4pZuYW9XshRKOq9z7iQghRXZxp2b5kHKzMGNLOvV7rTZNzivlkw2lWRaegKKBWQSdfBw4m5fFXTDp/xaQzKNSNpwe0oqOvw2X7mHdhbfiD3f1wsZHCjUI0hVAPO9QqyCosI7OgDHc7ebMHVTN03lx7gnKdnqScYkbN383ix7obPrgQQrRsybFH8KOQUsWUVt0G1iqRrewzgn3zx9MjZw09j7/F7i/P0Wvip9esUH5895/0yKsapCwb9hGmZtd+D6NSqwl9fAlnP+5LkD6B04sfxP65quJtUavm0CPvD3SKitTBcwn3adydq0T9Se16IUS93RLigloFJ9K0PP3DIXq+vYnXV8dwIk1bq+szC0p59bdjDPhoK78cqkrCh4V7sH56X355sg/rpvVlREcvVCrYeCKTkXN38ujCfRxIzKnRT3RyHrvisjFRq5jUN6gxnqoQ4jIszTS0cpOCbf/2x9F0DifnYWWmoa2HLVmFZTz45R52x2U3d2hCiFpIj/kbgHiz1rUeTTYxNaP7U9+y238KABGpS9g/5wHKy0qveE15WSnWG14AYK/TnbTtPqjWMVrZ2GM+5scaxdvORP9NxyNvAbAvaCrht9xZ6/5E05NEXAhRb4PC3Nn6/G08PSAET3sL8oorWLwrgWFz/ubOz3fw/Z7Ey1ZUzy+p4IN1J+n3/la+3Z1IhU7h1lYurH6qD/PHdCXkwhv7Nh62fPZQZzbO6Mc9XbzRqFVsO32eUfN38/BXe9hztupN7bwtVaPhIzt5yxZKQjSxdt52AByVRByA8ko97687CcDjfYNZPiWCHoFOFJZV8ujCffx1LK2ZIxRCXFPyXgDynTvV6TKVWk3EY++xr+NsKhU13fPXc+rjoRTk51y2/cEfZuGvTyYbe9qO+bjOYXoHhZI0YC46RUWPvD/wWHUf5qoKDln1pueYWdfuQDQrlaIoSnMH0Ri0Wi329vbk5+djZ2fX3OEIccPT6RW2nznP8qhkNp7IoEJX9U+Lhama28M9ub+7Lx187FmyK5EF2+LIL6lK0Dv6OvBSZBt6h7hc8x6J2UXM2xLHzwfPUamv6r+znwOHkvJQqWDD9L6GJF4I0TQW7ohn1u/HGRTqztePdmvucJrdkl0JvLY6Bhcbc7a90B9rcxNKK3Q888Mh1h/PQK2Ct+5uz0M9ZGcHIVqq+FkdCNQncqj3XDoPGVOvPo5sWUnI1iexUpURpwnEbsKvuHoFGM6nnD2B85JbsVBVsL/Le3S7c0q9493z3av0ipsDwDmVB7bP7MTe8drvq0TjqG0eKom4EMLosgvLWHUohZ+ikjmTWWg4bqpRGRL0Vm42PB/ZhiFh7nUu8HQut5gF2+JYHnWOcl1VRdPIdu588YgkAUI0taiEHO5bsBsPOwv2/Gdgc4fTrApKK+j3wVZyisp5865wxvTyN5yr1Ol55ddj/BiVDMBzg1vz1IAQKXAnRAujzcvC5pMQ1CqFrCnHcPHwrXdfZ6L/xvHXMbiQRzqulD3wE/6hXVH0eo6+P4QOpVEcM+9Eu5e2XHMt+dUoej1750/GO2sn5fcsIrh9r3r3JRpOEnFJxIVodoqicCg5j+VRyaw5nEpRuQ4fR0umD2rNXZ2rppo3RHp+KQu2xRGTms97ozoQ5GpjpMiFELVVWFZJ+9fXoSgQ9fIgXG1v3mKJH60/xWebYwlytWbdtL6Yamq+sVYUhY/Wn+bzC8tpxvUO4NU7wlA38N9CIYTxHN36M+23jidF5Y73a6cb3F9q/El0396Nr5KKFmvODV1ISU4aXfdNo1wxIWPMZnxbdTRC5KKlqG0eKlXThRCNRqVS0cXPkS5+jrw6IozYzELaeNjWq7L65XjYW/D6ne2M0pcQon5szE0IdLHm7PkiYlLz6d/GrblDahYZ2lK++vssAC9Gtr0kCYeqfxOfj2yDs40Zb6w5zuJdCWQXlfPRfR0xM7n6aFimtpT9iblEJeRwPFXL3Z29eVCmtwthdIVxuwFIs+2AtxH68wpsS+6Tmzn5xd20rTxB8J9jKFZVFYA74D+eCEnCb1qSiAshmoSVmQkdfByaOwwhRCNo723P2fNFHEu5eRPxTzeeprRCT1d/RyLbuV+17WN9AnGyNuO55YdZcziVvOJyFozpirV51dsyvV4h9nwhUQk5HEjIJSoxh+Sckhp97I3PISmnmBci28j0diGMyCbzAAA67+5G69PR1ROL6Rs4NPcBOhfvxJwKklVedH7odaPdQ1x/JBEXQgghRIOEe9nzW3Qqx1Jqt3XhjeZMRgE/XVj7/Z/b29YqMR7ZyRsHKzOmfHeAv89k8fBXexjSzoP9CTkcSMxFW1pZo71KBW097Ojm74hGrWLxrgTmbY0jXVvKe6M6XHYEXghRN7rKSgJKT4AKXEJvNWrflta2dJixmj1fPYV/xkaKhs7B19LaqPcQtXc2/ywbEzfiZePFsIBhaNTGma1ZF5KICyGEEKJBwr3tgZt3C7P3/jqFXqkqGtnV36nW1/Vr7cqyST0ZvziKw+fyOXzun9fP0lRDJ18Hugc40jXAic5+DthZmBrOh3ra8p9Vx/jlYArnC8qYP6YrNubytk6Ihkg6dYBAVQnFijn+ocYvAKsxMaHXEwsA8DR67+JaSipL2JC4gZ9P/8zBzIOG40tilvBi9xfp7mG8WRC1If9iCyGEEKJBwryqitGk5JWQW1SOo7VZM0fUdPbF57DxRAYatYoXh7at8/Wd/RxZMaU3r60+hr2lKV39nejm70iYl91VR7kf6O6Hm60FTy49yN9nsnjwy90sHNcdN1uLhjwdIW5qmcd3EAictQgl3PTm+XfsRncq5xQrT69k7dm1FFQUAKBWqenp0ZNjWcc4mXOS8evGM8B3AM91ew4/u6apvyGJuBBCCCEaxN7SFH9nKxKzi4lJ1XJLq5tj/1pFUXj7jxMAPNjdl+B67twQ4mbD0ol1327otrZu/Di5F+MXR3EsRcs983bx7fgejb6DRIVOT0puCf7OVrI+XdxQ1Of2AVDg0rmZI7n+pBSm8PXRrymuKObpzk/jY+vTrPEUVRTxZ/yf/Hz6Z45lHzMc97bx5u6Qu7kr5C7crd3JKc1hXvQ8Vp5eyebkzWxP2c7otqOZ3HEydmaNu/OWJOJCCCGEaLBwb3sSs4s5mpLf7Im4trSCP46k4W5nwW1tG6943J/H0olOzsPKTMOzg1o12n2upqOvAz8/0ZtHF+0jMbuYUfN38c247nTxczT6vUordCzfn8wX286SklfCu/e0l8rt4rqjLddeMcHy0B4BwCq4d1OGdEVphWkcyTrCAL8BmKpNr31BM8gqyeLLI1+y4vQKKvVVtS02J23miU5P8EjYI00ed0x2DCtOreCP+D8oqawqcmmiNmGA7wBGtR5FL89eqFX/zDZysnDilV6v8GCbB/lw/4fsTN3JkuNLWB23mic7Pcm9re/FRN04KbPsIy6EEEKIBpu/NY73/jrJ8A6ezH24S7PEcDxVy/d7E/n1UArF5ToA5o3uwu3tjb8as0KnZ/DH20jILuaZga2YMbi10e9RF1mFZYxfHMWRc/lYmKr57KEuDA67evX22tKWVvDd7kQW7Ywnq7DccHxoOw8WPNLVKPcQoinsSdvDtC3TmNxhMmNCx2Cm+Wf6ee75NBznVi0vyX/6NPbOxvn9qa8/4//kjd1vUFRRREfXjnzY70M8rD2aNaaL5ZflszhmMUtPLDUkvL08e6FTdESlRwHQ2rE1r0W8RgfXDo0eT1phGp8c+IQ/E/40HAuwC2BUq1GMCB6Bs6Vzrfr5+9zffLj/Q87mV21HGWwfzPPdn+cW71tqHUtt81BJxIUQQgjRYDvOZDHmm734O1ux7YXbmuy+ZZU6/jyaznd7EjmQmGs47mxtRnZROWYman6Y1LNORdRq49vdCbz6WwwuNmZsfeG2FlEoraiskqnLDrL11HnUKph9Vzije/rXu7+swjIW7ojnu92JFJRVjXR5O1jSr40ry/Ym4edkxfYXm+7vWoiGennHy6yOWw2An60fL3R/gX4+/VCpVBze+AMdd0whUe2D/6sxzRZjSWUJ7+17j5/P/AyAChUKCg7mDrx767v08e7TbLEBFFcUs+zkMhYeW0hBedV66/Yu7Xm2y7P09OyJoiisjlvNh/s/JK8sDxUqHmjzAM90eQZbM9tGiWfhsYUsjllMma4MFSqGBg7l/tb309W9a72Wz1ToK1h5eiXzoueRV5YHwC3et/BCtxcIcgi65vWSiEsiLoQQQjSZ3KJyOs/eAMDh14Zgb9m40xGTc4pZti+Jn6KSySmqGqU1UauIDPdgTE9/ugc4MuX7g2w8kYGjlSm/PNmHQBfjbBVUWFZJv/e3kF1UzuyR7XgkIsAo/RpDhU7Py6uOsnz/OQCeGRDCtEGtUatr/2b0XG4xX24/y09RyZRV6gFo5WbDE/2DGdHRi8LSSsPf9dHXh2Br0TKnzArxb3pFz5q4NXx68FOySrIA6O3Vmxe7v0jm8jlEpC5hn8Pt9Jj2Q7PEF5sbywvbXyA2LxYVKiZ3mMwdQXfw4vYXOZFzAhUqJnWYxJMdn2zy7bYqdBWsPLOSLw5/QXZpNgAhDiE83flpbvO97ZKEN7c0lw/3f2j44MPV0pX/6/F/DPYfbJTaEnpFz+9nf2fOgTlklmQC0M29Gy92f5FQ59AG9w9Vo/5fHvmSZSeXUamvxNbUlo33bcTK1Oqq10kiLom4EEII0aT6vLuZlLwSfpjUi4jg2k0DrAudXmH76fN8tyeRLacyqX4H42lvwUM9/Hiwuy9udv9UDS8ur+TBL/dw5Fw+/s5W/PJEb5xtzBscx8frT/G/zbEEulizfnrfFreHt6IofLLxDP/bdAYAjVqFi40ZLjbmuNiY42p78Z9muNqa42pjTlmlnoU741kdnUqlvurF7ejrwNT+wQwKda+RzEe8s4m0/FJWTImge4BxZxsI0diKKor46shXfHv8Wyr0FWhUGoYUaHg5O45T7V6jx6jpTRqPoiisil3FO3vfoVRXioulC+/c+g69PKuKOJbpynh/3/ssP70cgB4ePXiv73u4WDZ+PQ6dXsfa+LXMi55HSmEKUFXwbGqnqdweePs1PxDYm7aX2Xtmk6hNBKCfTz/+0/M/eNl41Tum6Mxo3tv3nqEIm7eNN893e56BfgMbpYBkojaRj/Z/RKhzKE90fOKa7SURl0RcCCGEaFJTvjvAXzHpvHx7KJP6Xnv6Xl1sO32eWWtiiDtfZDh2aysXxvTyZ2BbN0yukAyfLyjj7nk7OZdbQmc/B36Y1AsL0/qPJGVqS+n3wVZKKnTMH92FYY2w/txYftiXxOzfjxvWy9dFnxBnpvYPISLY+bJvbCcsjmLTyUxmjWzH2BY0I0CIukjWJvPh/g/ZnLwZAAedjoeDH2PyrTOabMS5sLyQWXtm8Wd81drm3l69eeuWty6bZK89u5Y3dr9BSWUJLpYuvN/3/Ubd+/pQ5iHe3PMmp3NPA+Bi6cKUDlO4p9U9mGpqPxOmTFfG10e/5uujX1Opr8TSxJKpnaYyOnR0nQqh/XsduLWpNZPaT2JM2BjMNQ3/kPVa9Iq+RqG3K5FEXBJxIYQQokl9vvkMH64/zchOXsx50Djb/yTnFDP79+OsP54BgJ2FCfd382V0L/9aTzWPzSxk1Pxd5JdUMCzcg7kPd6nTVO2LzfzlKD/sS6KznwO/PNG7xW/fVaHTk11YzvmCMrIKyzhfUMb5C39m/evP4nIdA0PdeLJ/CB19Ha7a74frTvH5llge7O7Lu6MavxCTEI1p5d9fsfTkh8SaVRVva+3Ymv/r8X+NmuRCVYXvF7e9SFJBEhqVhqc7P81j4Y9dNdk7m3+W57Y+R2xeLGqVmqc6PcWE9hNqlSDWVnZJNp8c+ITf4n4DwNbMlgnhE3g49GEsTSzr3e/ZvLPM2jOLAxkHAHAwd8DVyhUncyccLRwNXzUemztiY2bDytMra6wDv6fVPTzV+akmmRVQV7XNQ+tUWWT+/PnMnz+fhIQEANq1a8err77KsGHDyMnJ4bXXXmP9+vUkJSXh6urKXXfdxezZs7G3tzf0cbn/sH744QcefPBBw+OtW7cyY8YMYmJi8PX15ZVXXmHcuHF1CVUIIYQQTaydd9X/98dS8hvcV0m5jvnb4vhiWxxllXo0ahXjegfw7KBW2NVxTXKImw1fPtKVR77Zx5/H0nn7jxO8ckdYnfrIKy5nzqYz/BSVBMB/bg9t8Uk4gKlGjYe9BR72FtdsqyhKrZ9TmFfVm8vjadoGxSdES+CbksWKlHQ+dWrHKlcVp3NPM37deAb7D2ZKxym0djTurgiKorDs5DI+3P8hlfpKPK09eb/v+3Ry63TNa4Psg1h6+1Le2vsWq+NW879D/+Ng5kHeueUdHCwcGhSXTq9j5emVzDk0x1CI7Z5W9zCtyzQcLRq+JWKQQxALIxfyW+xvhmJu1cXQaqubezde6vESbZ3aNjie5lanRNzHx4d3332XVq1aoSgKS5YsYeTIkRw6dAhFUUhNTeXDDz8kLCyMxMREpkyZQmpqKitXrqzRz6JFixg6dKjhsYODg+H7+Ph4hg8fzpQpU1i6dCmbNm1i4sSJeHp6EhkZ2bBnK4QQQohGE+5VlYifzSqisKyyXpXEFUVhXUwGs38/Tkpe1ZY4fUKceX1EO1q517/ibs8gZz64rwPP/hjN1zvi8XWy4tHeAde8rkKn5/s9iXy68Qz5JRUAjO7pd0Oui67LBwuhnlWJ+Kn0Aip1+isuDRCisZRW6DiQmMvO2CxOphcw6dagetem0KREYQL0sbmViXfPZG70XJafXs6GxA1sSNxAd4/ujG47mv6+/Rs8ZT2tMI139r3DluQtAAzwHcCsPrOwN7e/xpX/sDK14s0+b9LNvRtv7X2LHSk7uO/3+/ig7we1SuYvJyYrhtl7ZhOTXVUxvq1TW17u+XK9+7sStUrN3a3uJjIgkkRtIrmlueSU5ZBbmlv1fWnV93lleVXfl+WSX5aPv50/07tMZ4DfgOviQ9DaaPDUdCcnJz744AMmTJhwybkVK1YwZswYioqKMDGp+s9YpVKxatUq7rrrrsv299JLL7F27VqOHTtmOPbggw+Sl5fHX3/9Veu4ZGq6EEII0fR6vb2JdG39injFZhbyxpoY/j5TVc3Yy96CV+4IY1i4h9HeeM3dEssH606hVsGCMV0Z0u7y+/IqisKmE5m8/ccJzmZVrUtv427LK3eEcmsrV6PEcj3T6xXCX19HcbmODdP7NuhDEiFqo0Kn58i5PHbFZrMzLouDiXmU6/SG8yFuNmyY3rde/1akvtEKLyWTowO+pX3fkQCczj3NgsML2Jy0GZ1SVWfBy9qLB9s+yD2t7qlT4pyQn8DGpI1sTNxoSHRN1aY83+15Hmr7UIP+fTuVc4rntj1nKIYWYBdAd4/udHPvRjePbrhZuV31+vyyfP538H+sOL0CBQUbUxue6vwUD7R5oE7rtxtTpb4SjUpz3STgjTI1/WI6nY4VK1ZQVFRERETEZdtU37w6Ca82depUJk6cSFBQEFOmTOGxxx4zvLC7d+9m0KBBNdpHRkYybdq0q8ZTVlZGWVmZ4bFWK1OlhBBCiKYW7m1PuraUo+fya52IF5RW8NnmWBbuiKdSr2BmoubxvkE80T8YKzPjvhF8sn8w53JL+GFfEs/8eIgfJ0fQ6V/roU+kaXlz7XF2xlZt0eNsbcaMIa15oJuvjPxeoFaraOthy8GkPI6naSURF0an1yucSNeyKzabXXFZ7IvPoehfhQfd7czpE+zCuph0YjML2RGbVecPyrJSE/FSMtEpKgI63mo43tqxNR/3/5j0onR+OvUTK0+vJLUolY8PfMy86HncEXwHD7d9mFaOrS7pU1EUTuWeYmPiRjYlbSI2L9ZwToWKru5djbbNVhunNvw4/Edm75nNn/F/kqBNIEGbwIrTK4CqxLyre1dDcu5u7Q5UFR77LfY3PjnwCblluQAMDxrOc12fw9WqZX3Y2FI+EDC2Oj+ro0ePEhERQWlpKTY2NqxatYqwsEvXWWVlZTF79mwmT55c4/isWbMYMGAAVlZWrF+/nieffJLCwkKeeeYZANLT03F3d69xjbu7O1qtlpKSEiwtL18g4J133uGNN96o69MRQgghhBGFe9ux8UQGx1KvvE5cURQytGWcyijgeKqWRTvjySyo+jB9UKgb/70jDH9n4+z5/W8qlYrZI9uRll/C1lPnmbgkilVP9sHXyYrzBWV8vOEUP0Ulo1fATKNm/C2BTL0tWPbKvowwLztDIj6yk3dzhyOuczq9wok0LXvjc9h7NpuohBxyiytqtHG0MiUi2JmIYBd6BzsT5GKNSqXitd+OsWR3Iot2JtQ5EU8+uhUXIFHjT5D9pR8eelh78GyXZ3m8w+P8Gf8nS08s5VTuKVaeXsnK0yvp4dGDh0Mfpq9PX2KyYtiUtImNiRs5V3jO0IeJyoSenj0Z6D+Q23xvM3qBMRszG97r+x7/6fkfDmYcJCojiv3p+zmZc9KQmP985mcA/Gz96ObRjfj8eA5lHgIg2D6Yl3u93OjF6URNdU7E27RpQ3R0NPn5+axcuZJHH32Ubdu21UjGtVotw4cPJywsjNdff73G9f/9738N33fu3JmioiI++OADQyJeXzNnzmTGjBk1YvD19W1Qn0IIIYSom+p14jEpVTPTsgrLOJ1RwOn0Ak5lFHImo4BTGQUUlFbWuC7A2YrXRrTjtrZXn0ZpDCYaNZ8/3IUHvthNTKqWRxft457O3izYdpbCsqq4hnfw5P+GtsXXyarR47lehXlW/V2fSCto5kjE9ahCp+dYSj774nPYG59DVELOJf8uWJtp6BHoRJ8QFyKCnQn1sLvsjgeP9g5gye5ENp/MJD6rqNY7KgCUnd0NwHnHTlxt00ULEwvubnU3d4XcxcHMgyw9sZTNSZvZl76Pfen7MFObUa4vN7Q315jTx6sPg/wH0denb52msteXvbk9t/ndxm1+twGgLddyMOMg+9P3E5URxcmckyQVJJFUUFV00tLEkikdp/BI6CN12o5MGEedE3EzMzNCQkIA6Nq1K1FRUcyZM4cvvvgCgIKCAoYOHYqtrS2rVq3C1PTqf6k9e/Zk9uzZlJWVYW5ujoeHBxkZGTXaZGRkYGdnd8XRcABzc3PMzRt//zghhBBCXFl7n6o3m6czC+g6ewPZReWXbadRqwh0saaNuy3dAxx5qKcf5iZNs28vgI25CQvHdefuuTs5e76ID9dX7ZPb0cee/94RRrcbsBibsYV6Vk1HP54qywHFtSmKwsGkPPaczWbP2WwOJOZesse9jbkJ3QIc6RnoTM8gJ9p722Nai+UgQa429G/jytZT5/l2dwKvjWhX67gccqIBUPv2qFV7lapqanlX9641pq3nleVhbWpNP59+DPIfRB+vPliZNu8HeXZmdvT37U9/3/4AFJQXcCjzEFHpUegVPY+EPYKH9eXrZIjG1+AJ93q93rA2W6vVEhkZibm5OatXr8bC4tpbZURHR+Po6GhIoiMiIvjjjz9qtNmwYcMV16ELIYQQouVwszXH18mS5JwSsovKUanAz8mK1u62tHa3obW7LW08bAl0sW7SxPty3O0sWPRYDx7+ag9mJmpeHNqGkR29673H+M2mrYcdalXVrIfMglLcbK/9vk/cfDK0paw8cI4V+5NJyC6ucc7e0pQegU70DHSiZ6AzoZ629a7D8FifQLaeOs+K/eeYMbh1rZaTlJUWE1R+BlTgGd6vzve8eNp6gjaBIPsgzDRm9Qm/Sdia2dLXpy99ffo2dyiCOibiM2fOZNiwYfj5+VFQUMCyZcvYunUr69atQ6vVMmTIEIqLi/n+++/RarWGgmmurq5oNBrWrFlDRkYGvXr1wsLCgg0bNvD222/z/PPPG+4xZcoUPv/8c1588UXGjx/P5s2bWb58OWvXrjXuMxdCCCGE0alUKhY/1oOj5/IJcrUmxM3G6AXXjKmNhy07/28AZhq1JOB1ZGmmIcDFmrPniziRViCJuDCo1OnZcuo8P0UlseXUeXT6qk2arM009G3tWpV4BznTxt3WaL93t4a4EORa9fP484FzjOsTeM1rEo7too2qkhzs8A66tOZVbVmYWNwQ+1qLplWn/xkzMzMZO3YsaWlp2Nvb06FDB9atW8fgwYPZunUre/fuBTBMXa8WHx9PQEAApqamzJ07l+nTp6MoCiEhIXz88cdMmjTJ0DYwMJC1a9cyffp05syZg4+PD19//bXsIS6EEEJcJ4JdbQh2tWnuMGrNwrR5R+avZ2Gedpw9X8TxVC39WresSsui6cVnFfFTVDI/HzzH+YJ/djPq6u/IA919Gd7eE2vzxvlgTq1WMa53AK/+FsOS3YmMjQi4ZpKfe2onAIlW4TipZUcE0bTq9JvwzTffXPFc//79udaW5EOHDmXo0KHXvE///v05dOhQXUITQgghhBBNLNTTjt+PpHEiTdaJ36xKynX8cTSNn/Ynsy8+x3DcxcaMe7r4cH83H0LcmmZ7u1FdfPjgr1PEZxWx7fT5axZ/NEvbD0CpR9emCE+IGlruXDEhhBBCCNGihXnZAXBcEvGbTkFpBYt3JvDV32fRXqh2rlZBv9auPNDdj4GhbrUqtGZM1uYm3N/dl292xLNoV8JVE3FFr8en8BgA9q36NFWIQhhIIi6EEEIIIeolzLMqET97vpDSCp1M878JFJdXsmRXIl9sjyPvwj7fvk6WPNDNl1FdffC0v/IuR03h0YgAFu6MZ/vp88RmFhLidvllMunJZ/AkhwpFQ2CHW5o4SiEkERdCCCGEEPXkZmuOs7UZ2UXlnEovoKOvQ3OHJBpJaYWO7/ckMn9rnGFbwiBXa6YNas0d7T1bTLFDP2crBrZ1Z+OJDJbsSmD2XeGXbZdybDueQIJpEK2sm2bqvBAXk6oEQgghhBCiXlQqFaEXRsVlnfiNqbRCx+Kd8dz6/hbeXHuC7KJy/J2t+Pj+jqyf1pc7O3q1mCS82vg+AQD8fPAc+SUVl22jS9gDQLZjpyaKSoiaZERcCCGEEELUW5iXHTtis2Sd+A2mvFLP8v3JzN0SS1p+KQDeDpY8MzCEe7r4NPn677qICHamtbsNpzMKWbE/mYm3Bl3Sxik3GgCTgF5NHJ0QVSQRF0IIIYQQ9RYmI+I3nNWHU3n/r5Ocyy0BwMPOgqcGhHB/N1/MTFpuAl5NpVIxrncg/1l1lCW7E3isTyCai0btSwq1BFacBRX4tO/XjJGKm1nL/00SQgghhBAt1j9T0wvQ66++la1o+aKT83jmh0Ocyy3B1dac10eEsfWF/ozp5X9dJOHV7u7sjb2lKck5JWw6kVHjXPyRHZio9GTihLtPcDNFKG52189vkxBCCCGEaHGCXK0xM1FTWFZJcm5xc4cjGujnA+cAGNjWje0v3Ma4PoHXZTV8SzMND/bwBWDxroQa5/LP7ATgnE04KrWkQ6J5yE+eEEIIIYSoN1ONmtbuVVtEyfT061uFTs/vR1IBGNs7AEuz6y8Bv9jYiADUKtgVl82p9ALDccuMAwCUe3ZvrtCEkERcCCGEEEI0TPU68eOpkohfz7afPk9ucQUuNub0CXZu7nAazNvBksh2HgAs3hUPgKLX418cA4BDmz7NFpsQkogLIYQQQogGqV4nfjyt4BotRUv2a3TVaPiIjp6YtOCq6HUxrncAAKsOpZBbVE7K2Rgc0VKmmBIYHtG8wYmb2o3xGyaEEEIIIZqNVE6//hWWVbLheDoAd3XybuZojKdHoBNhnnaUVuj5MSqZtGPbAThr1gpzC6tmjk7czCQRF0IIIYQQDdL2QiKekldCfnFFM0cj6mN9TDqlFXoCXazp4GPf3OEYjUqlYlyfAAC+252ALnEvAPnOnZsxKiEkERdCCCGEEA1kb2mKj6MlAMdlVPy6VD0tfWQnL1Qq1TVaX1/u7OiFk7UZqfmlOOQcBMAssFczRyVudpKICyGEEEKIBpPp6devzIJSdpw5D9xY09KrWZhqeLiHHzYU05qq7dn8OvRv3qDETU8ScSGEEEII0WD/FGyTRPx68/vhNPQKdPJ1IMDFurnDaRRjevnTVROHWqWQqnLHxdOvuUMSNzlJxIUQQgghRIOFecmI+PXqt+gUAO7q5NXMkTQeD3sL7veoKkaXatu+maMRQhJxIYQQQghhBNVT089kFFJeqW/maERtxWcVcfhcPhq1ijs63riJOECfXhGctOuDf/c7mjsUITBp7gCEEEIIIcT1z8fREltzEwrKKok7X2iYqi5atl8PVY2G3xLigouNeTNH07gcuj+AQ/cHmjsMIYA6jojPnz+fDh06YGdnh52dHREREfz555+G86WlpUydOhVnZ2dsbGwYNWoUGRkZNfpISkpi+PDhWFlZ4ebmxgsvvEBlZWWNNlu3bqVLly6Ym5sTEhLC4sWL6/8MhRBCCCFEo1OpVIbkW6anXx8URflnWnrnG3s0XIiWpk6JuI+PD++++y4HDhxg//79DBgwgJEjRxITEwPA9OnTWbNmDStWrGDbtm2kpqb+f3v3Hhdlmf9//DUcRRAQ4iAKnshTKKm1ibYarib91LS01K1VUisVKzXLtay0/RVbZmW5brWRdoJS18MqpRkskEm7xuYBDyQli5uAZALiYYCZ+f5BzEbiBggzDL6fjwePmPu+5r4/F3N7d33muu7r4vbbb7e+32QyMXr0aCoqKti9ezdvv/02a9eu5cknn7SWOXbsGKNHjyY6Opq9e/cyb948Zs6cyY4dO5qoyiIiIiLSHGqeEz90Qom4I9j3n1LyTp3Dw9WZm/sE2zsckSuKwWKxWC7nAH5+fixfvpyJEycSEBBAYmIiEydOBODIkSP07t2bzMxMBg0axMcff8yYMWM4ceIEQUFBALz22mssWrSI4uJi3NzcWLRoEcnJyWRnZ1vPMXnyZEpKSti+ffsl4zAajRiNRuvrsrIyQkNDKS0txdtbQ6NEREREmtuHe/JZ9NcDDAn35/2ZWqe5pVv6t4Os3Z3HuGtDWDm5v73DEWkVysrK8PHx+cU8tNGTtZlMJj744APOnj1LVFQUWVlZVFZWMmLECGuZXr16ERYWRmZmJgCZmZn07dvXmoQDjBo1irKyMmuvemZmZq1j1JSpOcalxMfH4+PjY/0JDQ1tbNVEREREpBH6dPABqnvEL7OvR5pZlcnMtv0ngNa5drhIS9fgRPzAgQN4eXnh7u7OrFmz2LRpE3369KGwsBA3Nzd8fX1rlQ8KCqKwsHqpgMLCwlpJeM3+mn3/q0xZWRnnz5+/ZFyLFy+mtLTU+nP8+PGGVk1ERERELsPVQV44Oxk4fa6SojLjL79B7GZX7vd8X16Bn6cbN159lb3DEbniNHjW9J49e7J3715KS0vZsGED06ZNIz09vTliaxB3d3fc3Vv3TI8iIiIiLVkbV2e6B3jydVE5hwpKCfZpY++Q5BK27K3uDR/TrwOuzlrRWMTWGvyvzs3NjfDwcAYOHEh8fDyRkZGsXLmS4OBgKioqKCkpqVW+qKiI4ODqyR+Cg4MvmkW95vUvlfH29sbDw6Oh4YqIiIiIDfWxzpx+xs6RyKWcq6hix8Hq0ajjNCxdxC4u++svs9mM0Whk4MCBuLq6kpKSYt2Xk5NDfn4+UVFRAERFRXHgwAFOnjxpLbNz5068vb3p06ePtcxPj1FTpuYYIiIiItJy1SxhppnTW66dh4o4V2EizK8tA8J87R2OyBWpQUPTFy9ezC233EJYWBhnzpwhMTGRtLQ0duzYgY+PDzNmzGDBggX4+fnh7e3NAw88QFRUFIMGVc+aefPNN9OnTx9+97vf8fzzz1NYWMiSJUuIi4uzDiufNWsWq1at4tFHH2X69Omkpqaybt06kpOTm772IiIiItKkrEuYaS3xFmvzVz+uHX5tCAaDwc7RiFyZGpSInzx5kqlTp1JQUICPjw/9+vVjx44djBw5EoCXXnoJJycnJkyYgNFoZNSoUaxevdr6fmdnZ7Zt28bs2bOJiorC09OTadOm8fTTT1vLdO3aleTkZObPn8/KlSvp1KkTb775JqNGjWqiKouIiIhIc6npEc87dZazxio83Rs8JZE0o1PlRjKOfg/AuP4ali5iL5e9jnhLVd/120RERESkaf3qmU85ecbIX2cPZmDn9vYOR37incw8ntxykL4dfdj6wI32Dkek1Wn2dcRFREREROrS2zphm4antzQ1w9LHXRti50hErmxKxEVERESkSek58ZYp/9Q5/pVfgpMBbo1UIi5iT0rERURERKRJqUe8Zdqyt7o3fEj4VQR6a413EXtSIi4iIiIiTapmLfEjBWcwmVvldEQOx2KxsHlvzbB0TdImYm9KxEVERESkSXW9ypM2rk6crzTx71Nn7R2OANnflfFN8VncXZwYdU2QvcMRueIpERcRERGRJuXsZKBnsJ4Tb0lqesNH9AmiXRtXO0cjIkrERURERKTJ1QxPP3RCibi9GatMbN13AoDxGpYu0iIoERcRERGRJtenQzvAvhO2VVSZWbxxP0Of/zsfHSiwWxz2VGUy82DSV5w8Y8Tf041hPQLsHZKIoERcRERERJqBvZcwO1dRxcx3viTpn8fJ/+Ecc97/Fws+3EvZhUq7xGMPZrOFhev3seNgEW7OTqyc3B83FzX/RVoC/UsUERERkSZX84x4UZmRU+VGm5779NkKfvuXf5DxdTEers5Mui4UJwNs/Oo7Yl7KYHfu9zaNxx4sFguPb85m894TuDgZWH3XAG68+ip7hyUiP1IiLiIiIiJNzsvdhS7+bQE4XHDGZuf9ruQ8E1/bzd7jJfi2dSXx3ht4bmI/1s8aTGf/tpwovcBv3/wHf9h2iAuVJpvFZUsWi4Vnkg+T9M98DAZ4adK1jOijmdJFWhIl4iIiIiLSLGqGp7/wSQ6FpRea/Xy5J88w8c+7+ab4LB182rBhVhT9w9oDMLBzez568Nf89oYwABJ2HWPsq7vI/q602eOytZc/Pcqbu44B8Nzt/RgbGWLniETk55SIi4iIiEizuH9od7zbuLD3eAljXv2MzG9ONdu5vso/zcTXMikovUD3AE/+Onsw4YHtapXxdHfh2dv68lbsdVzl5c7Rk+Xctvpz/vT3XExmS7PFZkuvp3/DypSjACwd24c7rw+1c0QiUhcl4iIiIiLSLCJDfdn6wI307uDN9+UV3J3wD97I+AaLpWmT3vSvi/ntX/5ByblKrg31ZcOswYT4elyy/PBeQXwyfygx1wRTabKwfEcOd76eyb9PnW3SuGzt3cw84j8+AsCjMT2JHdLVzhGJyKUoERcRERGRZtPZ35ONswdze/+OmMwWnv3oCHGJ/6LcWNUkx9+y9ztmrN3D+UoTQ3sEkHjvDbT3dPvF9/l5uvHnuwew4o5IvNxdyPr3aW5Z+RmJ/8hv8i8KbGFD1n94YstBAOZGhzPnpnA7RyQi/4vB4oh3mnooKyvDx8eH0tJSvL297R2OiIiIyBXNYrHw3hf/5ulth6g0Wege4Mnrvxt40fDxhljz+TGWbT0EwLhrQ1g+MbJRy3P95/Q5Fq7fxxff/gDAsB4B/HFCXzr4XLpXvSX56EABcxP/hdkC9wzpwpNj+mAwGOwdlsgVqb55qBJxEREREbGZrH+fZs77WRSVGfF0c2b5HZH8v74dGnQMi8XCik++ZtXfcwGIHVydfDo5NT75NJstvPX5MZ7fkUNFlZl2bVx4auw1TBjQsUUntalHirjvnSyqzBYmXx9K/O19W3S8Iq2dEnEl4iIiIiItUvEZIw8k/cvaA33f0G48OqonLs5192afNVZx8EQZB74rJfu7Uvb9p4Rvi6uf535kVE/m3NS9yZLP3JPlPLx+H/uOlwAwoncgz97Wl0DvNk1y/KZSZTLz2dHvuf+9LCqqzNwaGcJLk67F+TK+jBCRy9csiXh8fDwbN27kyJEjeHh4MHjwYJ577jl69uwJQF5eHl271j0pxLp167jjjjuqT1rHjTIpKYnJkydbX6elpbFgwQIOHjxIaGgoS5YsITY2tr6hKhEXERERacGqTGaW78jh9YxvARjUzY9XpwygjasTB0+Ukf1j0n3gu1K+/f4sP2+xujobWHZrhHU5sqaO7Y3PvuXlnUepMJnx8XDl6XHXcGtkiM17my0WC0VlRo4UlpFTeIacojPkFJ7h6MlyKqrMAIzsE8TquwbgeokvMkTEdpolEY+JiWHy5Mlcf/31VFVV8dhjj5Gdnc2hQ4fw9PTEZDJRXFxc6z1vvPEGy5cvp6CgAC8vr+qTGgysWbOGmJgYazlfX1/atKn+pvHYsWNEREQwa9YsZs6cSUpKCvPmzSM5OZlRo0Y16R9AREREROznowMFPLJ+H2crTHi4OnO+0lRnuWDvNkR09KFvRx/6dvImspMv/l7uzRpbTuEZHl6/l+zvygCIuSaY/39bBFc103lLzlVw9GR5dcJd81N0htLzlXWW93B15paIYOIn9MXdxblZYhKRhrHJ0PTi4mICAwNJT09n6NChdZbp378/AwYMICEh4b8nNRjYtGkT48ePr/M9ixYtIjk5mezsbOu2yZMnU1JSwvbt2+sVmxJxEREREceQe7Kc+9/9km9+HG4e4vPfpDuikw8RIT4EtGvepPtSKk1mXkv7hldSj1JpsuDn6cYfxkUwul/Dnmv/qdNnqxPur4vOkPvjf4+eLKf4jLHO8s5OBrpe5UnP4Hb0DGpHz+B29ApuR2j7tpf1XLyIND2bJOK5ublcffXVHDhwgIiIiIv2Z2Vlcd111/H5558zePDg/57UYCAkJASj0Ui3bt2YNWsW99xzj3Woz9ChQxkwYAAvv/yy9T1r1qxh3rx5lJaW1hmL0WjEaPzvzausrIzQ0FAl4iIiIiIO4EKliSOFZwht79HsPd2NcehEGQ+v38fhgure8TH9OvDEmD64OBk4V2HifKWJcxUmzlVUcb6i+vfzP74+V2miqPQCXxeVc/RkOd+X151wA3T09eDqIC9rst0jqB3dA7xo46oebxFHUN9E3KWxJzCbzcybN48hQ4bUmYQDJCQk0Lt371pJOMDTTz/N8OHDadu2LZ988glz5syhvLycBx98EIDCwkKCgoJqvScoKIiysjLOnz+Ph8fFS0nEx8ezbNmyxlZHREREROyojasz14b62juMS+oT4s2WuCGsSj3Kn9K+Ydv+ArbtL2j08Tr6etAjyIurg9pxdWD1f8MDvfByb3TzXEQcSKP/pcfFxZGdnc2uXbvq3H/+/HkSExN54oknLtr30239+/fn7NmzLF++3JqIN8bixYtZsGCB9XVNj7iIiIiISFNwc3Fiwc09GdknmEc27ONI4RkA3F2caOvmTFs3FzzcnGnr5oyHq3Otbf6ebtakOzzQC08l3CJXtEbdAebOncu2bdvIyMigU6dOdZbZsGED586dY+rUqb94vBtuuIE//OEPGI1G3N3dCQ4OpqioqFaZoqIivL296+wNB3B3d8fdveUNYxIRERGR1qVvJx8+fujXnK800cbFWc9pi0iDNWiNA4vFwty5c9m0aROpqamXXKoMqoel33rrrQQEBPzicffu3Uv79u2tiXRUVBQpKSm1yuzcuZOoqKiGhCsiIiIi0iwMBgNt3VyUhItIozSoRzwuLo7ExES2bNlCu3btKCwsBMDHx6dWT3Vubi4ZGRl89NFHFx1j69atFBUVMWjQINq0acPOnTt59tlnWbhwobXMrFmzWLVqFY8++ijTp08nNTWVdevWkZyc3Nh6ioiIiIiIiLQIDZo1vWZW859bs2YNsbGx1tePPfYY7733Hnl5eTg51e503759O4sXLyY3NxeLxUJ4eDizZ8/m3nvvrVU2LS2N+fPnc+jQITp16sQTTzxR6xy/RMuXiYiIiIiIiC3ZZPmylkyJuIiIiIiIiNhSffPQBj0jLiIiIiIiIiKXR4m4iIiIiIiIiA0pERcRERERERGxISXiIiIiIiIiIjbUoOXLHEnNHHRlZWV2jkRERERERESuBDX55y/Nid5qE/FTp04BEBoaaudIRERERERE5Epy6tQpfHx8Lrm/1Sbifn5+AOTn5//PP0BLcf3117Nnzx57h1FvjhRvWVkZoaGhHD9+vFUuZedIn4UjxarrpmVxtHjrwxGvMUf7HBwt3vpytGvH0T4HR4u3vhztugHH+iwcKdaG0HXTcKWlpYSFhVnz0UtptYm4k1P14+8+Pj4OcdE4Ozs7RJw1HC1eAG9vb4eLuT4c6bNwpFhr6LppGRwt3oZwpGvM0T4HR4u3oRzl2nG0z8HR4m0oR7luwLE+C0eKtTF03TRcTT56yf02ikN+QVxcnL1DaBBHi7c1c6TPwpFibe0c7bNwtHhbK0f7HBwt3tbK0T4HR4u3NXOkz8KRYm3tHOWzMFh+6SlyB1VWVoaPjw+lpaUt4hsRsR9dC9IYum6kuekak8bStSONoetGGkPXTcPV92/WanvE3d3deeqpp3B3d7d3KGJnuhakMXTdSHPTNSaNpWtHGkPXjTSGrpuGq+/frNX2iIuIiIiIiIi0RK22R1xERERERESkJVIiLiIiIiIiImJDSsRFREREREREbEiJuIhIAxgMBjZv3mzvMERERETsRu2hy+eQiXhsbCzjx4+3dxjSQmRmZuLs7Mzo0aPtHYo4IN1PpLkcP36c6dOnExISgpubG507d+ahhx7i1KlT9Xp/WloaBoOBkpKS5g1UWhTdk6Sx1B6Sy6F7j+05ZCIu8lMJCQk88MADZGRkcOLEics6lslkwmw2N1FkInKl+vbbb7nuuus4evQoSUlJ5Obm8tprr5GSkkJUVBQ//PCDvUMUkVZG7SERx+Lwifj27du58cYb8fX1xd/fnzFjxvDNN99Y9+fl5WEwGNi4cSPR0dG0bduWyMhIMjMz7Ri1NJXy8nI+/PBDZs+ezejRo1m7dq11X01vUnJyMv369aNNmzYMGjSI7Oxsa5m1a9fi6+vL3/72N/r06YO7uzv5+fl2qIm0BF26dOHll1+ute3aa69l6dKldolHHFdcXBxubm588sknDBs2jLCwMG655RY+/fRTvvvuOx5//HEAjEYjixYtIjQ0FHd3d8LDw0lISCAvL4/o6GgA2rdvj8FgIDY21o41EntQG0fqS+0haUpqD9mGwyfiZ8+eZcGCBXz55ZekpKTg5OTEbbfddtG3eI8//jgLFy5k79699OjRgylTplBVVWWnqKWprFu3jl69etGzZ0/uvvtu3nrrLSwWS60yjzzyCCtWrGDPnj0EBAQwduxYKisrrfvPnTvHc889x5tvvsnBgwcJDAy0dTVEpBX54Ycf2LFjB3PmzMHDw6PWvuDgYO666y4+/PBDLBYLU6dOJSkpiVdeeYXDhw/z+uuv4+XlRWhoKH/9618ByMnJoaCggJUrV9qjOmJHauNIfak9JOJ4XOwdwOWaMGFCrddvvfUWAQEBHDp0iIiICOv2hQsXWp+ZWbZsGddccw25ubn06tXLpvFK00pISODuu+8GICYmhtLSUtLT07npppusZZ566ilGjhwJwNtvv02nTp3YtGkTd955JwCVlZWsXr2ayMhIm8cvIq3P0aNHsVgs9O7du879vXv35vTp0+zZs4d169axc+dORowYAUC3bt2s5fz8/AAIDAzE19e32eOWlkdtHKkvtYdEHI/D94gfPXqUKVOm0K1bN7y9venSpQvARcNp+vXrZ/29Q4cOAJw8edJmcUrTy8nJ4Z///CdTpkwBwMXFhUmTJpGQkFCrXFRUlPV3Pz8/evbsyeHDh63b3Nzcal0fIiJN4ee9UT+Xl5eHs7Mzw4YNs1FE4mjUxpH6UHtIxDE5fI/42LFj6dy5M3/5y18ICQnBbDYTERFBRUVFrXKurq7W3w0GA4AmoXBwCQkJVFVVERISYt1msVhwd3dn1apV9T6Oh4eH9ZqQK5uTk9NFydNPh+2J1Ed4eDgGg4HDhw9z2223XbT/8OHDtG/f/qJh6yI/pzaO1IfaQ9LU1B6yDYfuET916hQ5OTksWbKE3/zmN9bhftL6VVVV8c4777BixQr27t1r/dm3bx8hISEkJSVZy37xxRfW30+fPs3XX399ySGjcmULCAigoKDA+rqsrIxjx47ZMSJxRP7+/owcOZLVq1dz/vz5WvsKCwt5//33mTRpEn379sVsNpOenl7ncdzc3IDq2YvlyqM2jtSH2kPSHNQesg2HTsTbt2+Pv78/b7zxBrm5uaSmprJgwQJ7hyU2sG3bNk6fPs2MGTOIiIio9TNhwoRaw7GefvppUlJSyM7OJjY2lquuukrrJEqdhg8fzrvvvstnn33GgQMHmDZtGs7OzvYOSxzQqlWrMBqNjBo1ioyMDI4fP8727dsZOXIkHTt25JlnnqFLly5MmzaN6dOns3nzZo4dO0ZaWhrr1q0DoHPnzhgMBrZt20ZxcTHl5eV2rpXYkto4Uh9qD0lzUHvINhwyETebzbi4uODk5MQHH3xAVlYWERERzJ8/n+XLl9s7PLGBhIQERowYgY+Pz0X7JkyYwJdffsn+/fsB+OMf/8hDDz3EwIEDKSwsZOvWrdaeJpGa+wnA4sWLGTZsGGPGjGH06NGMHz+e7t272zlCcURXX301X375Jd26dePOO++ke/fu3HfffURHR5OZmWmdiO3Pf/4zEydOZM6cOfTq1Yt7772Xs2fPAtCxY0eWLVvG73//e4KCgpg7d649qyQ2ojaONITaQ9JU1B6yPYPll2aTaYFiYmIIDw9v0HMvcuVJS0sjOjqa06dPa8ZhuSTdT0SkJdE9SZqa2kNSH7r32J5D9YifPn2abdu2kZaWZl3qRUSkMXQ/EZGWRPckEbEH3Xvsx6FmTZ8+fTp79uzh4YcfZty4cfYOR0QcmO4nItKS6J4kIvage4/9OOTQdBERERERERFH5VBD00VEREREREQcnRJxERERERERERtqsYl4fHw8119/Pe3atSMwMJDx48eTk5NTq8yFCxeIi4vD398fLy8vJkyYQFFRkXX/vn37mDJlCqGhoXh4eNC7d29WrlxZ6xgbN25k5MiRBAQE4O3tTVRUFDt27LBJHUVEROTKY6s2zq5duxgyZAj+/v54eHjQq1cvXnrpJZvUUURE/rcWm4inp6cTFxfHF198wc6dO6msrOTmm2+2rq8KMH/+fLZu3cr69etJT0/nxIkT3H777db9WVlZBAYG8t5773Hw4EEef/xxFi9eXGta/oyMDEaOHMlHH31EVlYW0dHRjB07lq+++sqm9RUREZErg63aOJ6ensydO5eMjAwOHz7MkiVLWLJkCW+88YZN6ysiIhdzmMnaiouLCQwMJD09naFDh1JaWkpAQACJiYlMnDgRgCNHjtC7d28yMzMZNGhQnceJi4vj8OHDpKamXvJc11xzDZMmTeLJJ59slrqIiIiI1LBlG+f222/H09OTd999t1nqIiIi9dNie8R/rrS0FAA/Pz+g+pvgysrKWuvd9erVi7CwMDIzM//ncWqOURez2cyZM2f+ZxkRERGRpmKrNs5XX33F7t27GTZsWBNFLiIijeUQ64ibzWbmzZvHkCFDiIiIAKCwsBA3Nzd8fX1rlQ0KCqKwsLDO4+zevZsPP/yQ5OTkS57rhRdeoLy8nDvvvLPJ4hcRERGpiy3aOJ06daK4uJiqqiqWLl3KzJkzm7weIiLSMA6RiMfFxZGdnc2uXbsafYzs7GzGjRvHU089xc0331xnmcTERJYtW8aWLVsIDAxs9LlERERE6sMWbZzPPvuM8vJyvvjiC37/+98THh7OlClTLidsERG5TC0+EZ87dy7btm0jIyODTp06WbcHBwdTUVFBSUlJrW+Mi4qKCA4OrnWMQ4cO8Zvf/Ib77ruPJUuW1HmeDz74gJkzZ7J+/fpaQ8FEREREmoOt2jhdu3YFoG/fvhQVFbF06VIl4iIidtZinxG3WCzMnTuXTZs2kZqaav2fSI2BAwfi6upKSkqKdVtOTg75+flERUVZtx08eJDo6GimTZvGM888U+e5kpKSuOeee0hKSmL06NHNUyERERERbNvG+Tmz2YzRaGyaioiISKO12B7xuLg4EhMT2bJlC+3atbM+E+Xj44OHhwc+Pj7MmDGDBQsW4Ofnh7e3Nw888ABRUVHW2USzs7MZPnw4o0aNYsGCBdZjODs7ExAQAFQPR582bRorV67khhtusJapOYeIiIhIU7JVG+dPf/oTYWFh9OrVC6hesvWFF17gwQcftEOtRUTkp1rs8mUGg6HO7WvWrCE2NhaACxcu8PDDD5OUlITRaGTUqFGsXr3aOmxr6dKlLFu27KJjdO7cmby8PABuuukm0tPTLyozbdo01q5d2yR1EREREalhqzbOq6++yuuvv86xY8dwcXGhe/fu3Hvvvdx///04ObXYQZEiIleEFpuIi4iIiIiIiLRG+jpURERERERExIaUiIuIiIiIiIjYkBJxERERERERERtSIi4iIiIiIiJiQ0rERURERERERGxIibiIiIiIiIiIDSkRFxEREREREbEhJeIiIiIiIiIiNqREXERERERERMSGlIiLiIi0QrGxsRgMBgwGA66urgQFBTFy5EjeeustzGZzvY+zdu1afH19my9QERGRK5AScRERkVYqJiaGgoIC8vLy+Pjjj4mOjuahhx5izJgxVFVV2Ts8ERGRK5YScRERkVbK3d2d4OBgOnbsyIABA3jsscfYsmULH3/8MWvXrgXgxRdfpG/fvnh6ehIaGsqcOXMoLy8HIC0tjXvuuYfS0lJr7/rSpUsBMBqNLFy4kI4dO+Lp6ckNN9xAWlqafSoqIiLiYJSIi4iIXEGGDx9OZGQkGzduBMDJyYlXXnmFgwcP8vbbb5Oamsqjjz4KwODBg3n55Zfx9vamoKCAgoICFi5cCMDcuXPJzMzkgw8+YP/+/dxxxx3ExMRw9OhRu9VNRETEURgsFovF3kGIiIhI04qNjaWkpITNmzdftG/y5Mns37+fQ4cOXbRvw4YNzJo1i++//x6ofkZ83rx5lJSUWMvk5+fTrVs38vPzCQkJsW4fMWIEv/rVr3j22WebvD4iIiKtiYu9AxARERHbslgsGAwGAD799FPi4+M5cuQIZWVlVFVVceHCBc6dO0fbtm3rfP+BAwcwmUz06NGj1naj0Yi/v3+zxy8iIuLolIiLiIhcYQ4fPkzXrl3Jy8tjzJgxzJ49m2eeeQY/Pz927drFjBkzqKiouGQiXl5ejrOzM1lZWTg7O9fa5+XlZYsqiIiIODQl4iIiIleQ1NRUDhw4wPz588nKysJsNrNixQqcnKqnjVm3bl2t8m5ubphMplrb+vfvj8lk4uTJk/z617+2WewiIiKthRJxERGRVspoNFJYWIjJZKKoqIjt27cTHx/PmDFjmDp1KtnZ2VRWVvLqq68yduxYPv/8c1577bVax+jSpQvl5eWkpKQQGRlJ27Zt6dGjB3fddRdTp05lxYoV9O/fn+LiYlJSUujXrx+jR4+2U41FREQcg2ZNFxERaaW2b99Ohw4d6NKlCzExMfz973/nlVdeYcuWLTg7OxMZGcmLL77Ic889R0REBO+//z7x8fG1jjF48GBmzZrFpEmTCAgI4PnnnwdgzZo1TJ06lYcffpiePXsyfvx49uzZQ1hYmD2qKiIi4lA0a7qIiIiIiIiIDalHXERERERERMSGlIiLiIiIiIiI2JAScREREREREREbUiIuIiIiIiIiYkNKxEVERERERERsSIm4iIiIiIiIiA0pERcRERERERGxISXiIiIiIiIiIjakRFxERERERETEhpSIi4iIiIiIiNiQEnERERERERERG/o/dVCYA0SM0FsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print the forecast values\n",
    "print(\"Forecast Values:\")\n",
    "print(prediction.forecast)\n",
    "\n",
    "def getfcast_value(cat, d):\n",
    "    try:\n",
    "        ret_val = round(prediction.forecast.loc[d.strftime(\"%Y-%m-%d\")][cat], 3)\n",
    "        return ret_val\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        return np.NaN\n",
    "\n",
    "df_all['f1'] = df_all.apply(lambda x: getfcast_value(x['Category'], x['Date']) if x['Date'] >= dt else np.NaN, axis=1)\n",
    "\n",
    "for cat in df_all.Category.unique():\n",
    "    df_all[(df_all.Category == cat) & (df_all.Date > '2022-01-01')].set_index('Date').drop('Category', axis=1).plot(\n",
    "        title=\"Category : \" + cat, figsize=(12, 3))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}