xlstm_ceemdan_full.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XLSTM-CEEMDAN model\n",
    "\n",
    "## Python requirements\n",
    "\n",
    "```\n",
    "!pip install plotly\n",
    "!pip install cufflinks\n",
    "!pip install chart_studio\n",
    "!pip install ipywidgets\n",
    "!pip install yfinance\n",
    "!pip install EMD-signal\n",
    "!pip install sklearn\n",
    "!pip install keras\n",
    "!pip install tensorflow\n",
    "```\n",
    "\n",
    "## Model\n",
    "\n",
    "The **XLSTM-CEEMDAN** works with daily data from equities: open, high, low, close, volume. Hereinafter we are calling these \"features\".\n",
    "\n",
    "### Data processing steps:\n",
    "\n",
    "1. Data gathering\n",
    "\n",
    "We use yfinance package to get the daily ticker history. We obtain a dataframe with one time series for each of the features, for each of the tickers we are working with.\n",
    "\n",
    "Here we are considering the top 10 most liquid tickers in Brazilian B3 exchange in period of **2019-01-01 until 2020-05-01**:\n",
    "\n",
    "**PETR4, VALE3, BOVA11, ITUB4, BBDC4, B3SA3, BBAS3, ABEV3, MGLU3, VVAR3**\n",
    "\n",
    "2. Decomposition\n",
    "\n",
    "We decompose the data with the complete ensemble empirical mode decomposition with adative noise (CEEMDAN) algorithm, using the [PyEMD public package from Dawid Laszuk](github.com/laszukdawid/PyEMD).\n",
    "\n",
    "As a result, we obtain a set of instrinsic mode functions (IMFs) time series for each of the features, for each of the tickers. The number of IMFs resulting from a decomposition may vary, and depends upon CEEMDAN hyperparameters such as scale of added noise, specific series characteristics, and mainly **series length**.\n",
    "\n",
    "3. Data transformation\n",
    "\n",
    "For each of the IMFs series obtained, we tranform the data according to the following equation in order to work with data values between 0 and 1.\n",
    "\n",
    "$$x' = \\frac{x-x_{min}}{x_{max}-x_{min}}$$\n",
    "\n",
    "Where x is any element in the series to be transformed, x' is the equivalent transformed element. The equation also takes into account the maximum and minimum values of the series.\n",
    "\n",
    "4. Windowing\n",
    "\n",
    "Besides the mathematical transformation, each series undergo a vectorial transformation in order to split the data into windows.\n",
    "\n",
    "The process is examplified:\n",
    "\n",
    "```\n",
    "original single dimensional series = [1,2,3,4,5,6,7,8]\n",
    "Splitting into windows of length 4.\n",
    "Windowed data = [[1,2,3,4],[2,3,4,5],[3,4,5,6],[4,5,6,7],[5,6,7,8]]\n",
    "```\n",
    "This way the resulting series will have $n-w+1$ elements, where $n$ is the length of the orginal series and $w$ is the window length.\n",
    "\n",
    "### Processed data availability\n",
    "\n",
    "After the processing method, the final data is available in a dataframe format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": "        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        requirejs.config({\n            paths: {\n                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n            }\n        });\n        require(['plotly'], function(Plotly) {\n            window._Plotly = Plotly;\n        });\n        }\n        </script>\n        "
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": "        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        requirejs.config({\n            paths: {\n                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n            }\n        });\n        require(['plotly'], function(Plotly) {\n            window._Plotly = Plotly;\n        });\n        }\n        </script>\n        "
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": "        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        requirejs.config({\n            paths: {\n                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n            }\n        });\n        require(['plotly'], function(Plotly) {\n            window._Plotly = Plotly;\n        });\n        }\n        </script>\n        "
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "Using TensorFlow backend.\n"
    }
   ],
   "source": [
    "from datetime import timedelta, datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import cufflinks as cf\n",
    "import chart_studio.plotly as plotly\n",
    "import plotly.offline\n",
    "cf.go_offline()\n",
    "cf.set_config_file(offline=True, world_readable=False)\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error \n",
    "from keras.preprocessing.sequence import TimeseriesGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM, LeakyReLU\n",
    "import yfinance as yf\n",
    "from PyEMD import CEEMDAN\n",
    "\n",
    "experiment_time = datetime.now().strftime(\"%H_%M_%S_%m_%d_%Y\")\n",
    "\n",
    "# iplot layout\n",
    "space =  {\n",
    "            'legend' : {'bgcolor':'#1A1A1C','font':{'color':'#D9D9D9',\"size\":12}},\n",
    "            'paper_bgcolor' : '#1A1A1C',\n",
    "            'plot_bgcolor' : '#1A1A1C',\n",
    "            \"title\" : {\"font\":{\"color\":\"#D9D9D9\"},\"x\":0.5},\n",
    "            'yaxis' : {\n",
    "                'tickfont' : {'color':'#C2C2C2', \"size\":12},\n",
    "                'gridcolor' : '#434343',\n",
    "                'titlefont' : {'color':'#D9D9D9'},\n",
    "                'zerolinecolor' : '#666570',\n",
    "                'showgrid' : True\n",
    "            },\n",
    "            'xaxis' : {\n",
    "                'tickfont' : {'color':'#C2C2C2', \"size\":12},\n",
    "                'gridcolor' : '#434343',\n",
    "                'titlefont' : {'color':'#D9D9D9'},\n",
    "                'zerolinecolor' : '#666570',\n",
    "                'showgrid' : True\n",
    "            },\n",
    "            'titlefont' : {'color':'#D9D9D9'}\n",
    "        }\n",
    "\n",
    "features_in_order = ['Open', 'High', 'Low', 'Volume', 'Adj Close'] # target feature must be the last one here\n",
    "target_feature = 'Adj Close'\n",
    "\n",
    "class ManyToOneTimeSeriesGenerator(TimeseriesGenerator):\n",
    "  def __getitem__(self, idx):\n",
    "    x, y = super().__getitem__(idx)\n",
    "    last_element_index = y.shape[1]-1\n",
    "    return x, y[:,last_element_index].reshape(1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n"
    }
   ],
   "source": [
    "tickers = [\"PETR4\", \"VALE3\", \"BOVA11\", \"ITUB4\", \"BBDC4\", \"B3SA3\", \"BBAS3\", \"ABEV3\", \"MGLU3\", \"VVAR3\"]\n",
    "\n",
    "tickers = [f\"{ticker}.SA\" for ticker in tickers]\n",
    "\n",
    "start_datetime = datetime(year=2018, month=7, day=20)\n",
    "end_datetime = datetime(year=2019, month=12, day=2)\n",
    "\n",
    "history_data = {ticker.split('.')[0]:yf.download(ticker, start=start_datetime, end=end_datetime).drop(['Close'], axis=1).dropna()[features_in_order] for ticker in tickers}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[PETR4] Decomposing...\n[VALE3] Decomposing...\n[BOVA11] Decomposing...\n[ITUB4] Decomposing...\n[BBDC4] Decomposing...\n[B3SA3] Decomposing...\n[BBAS3] Decomposing...\n[ABEV3] Decomposing...\n[MGLU3] Decomposing...\n[VVAR3] Decomposing...\nCPU times: user 5min 50s, sys: 5.83 s, total: 5min 56s\nWall time: 10min 36s\n"
    }
   ],
   "source": [
    "%%time\n",
    "ceemdan = CEEMDAN()\n",
    "decomposed_data = {}\n",
    "decomposed_ticker_features_series = {}\n",
    "scalers = {}\n",
    "for ticker in history_data:\n",
    "    # iterating every ticker\n",
    "    print(f'[{ticker}] Decomposing...')\n",
    "    ticker_dataframe = history_data[ticker]\n",
    "    decomposed_ticker_features_series[ticker] = {}\n",
    "    scalers[ticker] = {}\n",
    "    for column in ticker_dataframe.columns:\n",
    "        # iterating evey feature\n",
    "        scaler = MinMaxScaler()\n",
    "        decomposed_ticker_features_series[ticker][column] = {}\n",
    "        series = ticker_dataframe[column].values.reshape(-1,1)\n",
    "        scaler.fit(series)\n",
    "        scalers[ticker][column] = scaler\n",
    "        ticker_feature_time_series = np.frombuffer(scaler.transform(series))\n",
    "        ticker_feature_time_series_imfs = ceemdan(ticker_feature_time_series, max_imf=10)\n",
    "        for i, imf_series in enumerate(ticker_feature_time_series_imfs):\n",
    "            # iterating every IMF \n",
    "            if i < len(ticker_feature_time_series_imfs):\n",
    "                decomposed_ticker_features_series[ticker][column][f'IMF{i+1}'] = imf_series\n",
    "            else:\n",
    "                decomposed_ticker_features_series[ticker][column][f'Rsd'] = imf_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "outputPrepend",
     "outputPrepend",
     "outputPrepend"
    ]
   },
   "outputs": [],
   "source": [
    "# Data organisation\n",
    "\n",
    "max_window_size = 10\n",
    "windows_sizes_for_imf_level = {\n",
    "    'IMF1': 2,\n",
    "    'IMF2': 2,\n",
    "    'IMF3': 3,\n",
    "    'IMF4': 3,\n",
    "    'IMF5': 4,\n",
    "    'IMF6': 4,\n",
    "    'IMF7': 5,\n",
    "    'IMF8': 5,\n",
    "    'Rsd': 6,\n",
    "    'DEFAULT': 4\n",
    "}\n",
    "\n",
    "target_feature_max_imf_level = {}\n",
    "\n",
    "# Coupling together the IMFs of the same level for different features to create exogenous input\n",
    "# The number of imfs for each feature decomposition may differ, thus some of the last imfs may not match in number of features\n",
    "series = {}\n",
    "for ticker in decomposed_ticker_features_series:\n",
    "    series[ticker] = {}\n",
    "    for feature in decomposed_ticker_features_series[ticker]:\n",
    "        imfs = pd.DataFrame.from_dict(decomposed_ticker_features_series[ticker][feature])\n",
    "        for imf in imfs:\n",
    "            if imf not in series[ticker]:\n",
    "                series[ticker][imf] = []\n",
    "            _series = imfs[imf].values\n",
    "            _series = _series.reshape((len(_series),1)) # reshaping to get into column format\n",
    "            series[ticker][imf] += [_series]\n",
    "            if feature == target_feature:\n",
    "                target_feature_max_imf_level[ticker] = imf\n",
    "                \n",
    "# cut_spare_imfs: when any of the exogenous features have more imfs than the target feature. This solves a bug, if not excluded, these spare imfs from exogenous features would be wrongly added in the recomposition of the target feature.\n",
    "series_cut = {}\n",
    "for ticker in series:\n",
    "    if ticker not in series_cut:\n",
    "        series_cut[ticker] = {}\n",
    "    for imf_level_string in series[ticker]:\n",
    "        imf_level_int = int(imf_level_string[3:]):\n",
    "        if imf_level_int > int(target_feature_max_imf_level[ticker][3:]):\n",
    "            continue\n",
    "        else:\n",
    "            series_cut[ticker][imf_level_string] = series[ticker][imf_level_string].copy()\n",
    "\n",
    "series = series_cut\n",
    "dataset = {}\n",
    "# # horizontal stack\n",
    "for ticker in series:\n",
    "    dataset[ticker] = {}\n",
    "    for imf_level in series[ticker]:\n",
    "        dataset[ticker][imf_level] = np.hstack(tuple(series[ticker][imf_level]))\n",
    "\n",
    "# # data set split rates\n",
    "train = 0.7\n",
    "validation = 0.2\n",
    "test = 0.1\n",
    "\n",
    "train_dataset = {}\n",
    "validation_dataset = {}\n",
    "test_dataset = {}\n",
    "\n",
    "train_generators = {}\n",
    "validation_generators = {}\n",
    "test_generators = {}\n",
    "\n",
    "for ticker in dataset:\n",
    "\n",
    "    train_dataset[ticker] = {}\n",
    "    validation_dataset[ticker] = {}\n",
    "    test_dataset[ticker] = {}\n",
    "\n",
    "    train_generators[ticker] = {}\n",
    "    validation_generators[ticker] = {}\n",
    "    test_generators[ticker] = {}\n",
    "\n",
    "    for imf_level in dataset[ticker]:\n",
    "        # splitting data sets according to rates\n",
    "        train_dataset[ticker][imf_level] = dataset[ticker][imf_level][:round(train*dataset[ticker][imf_level].shape[0]),:]\n",
    "        validation_dataset[ticker][imf_level] = dataset[ticker][imf_level][round(train*dataset[ticker][imf_level].shape[0]):round((train+validation)*dataset[ticker][imf_level].shape[0]),:]\n",
    "        test_dataset[ticker][imf_level] = dataset[ticker][imf_level][round((train+validation)*dataset[ticker][imf_level].shape[0]):,:]\n",
    "\n",
    "        if imf_level in windows_sizes_for_imf_level:\n",
    "            window_size = windows_sizes_for_imf_level[imf_level]\n",
    "        else: \n",
    "            window_size = windows_sizes_for_imf_level['DEFAULT']\n",
    "        # windowing\n",
    "        train_generators[ticker][imf_level] = ManyToOneTimeSeriesGenerator(train_dataset[ticker][imf_level], train_dataset[ticker][imf_level], length=window_size, batch_size=1)\n",
    "        validation_generators[ticker][imf_level] = ManyToOneTimeSeriesGenerator(validation_dataset[ticker][imf_level], validation_dataset[ticker][imf_level], length=window_size, batch_size=1)\n",
    "        test_generators[ticker][imf_level] = ManyToOneTimeSeriesGenerator(test_dataset[ticker][imf_level], test_dataset[ticker][imf_level], length=window_size, batch_size=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Training model [PETR4][IMF1]\nTraining model [PETR4][IMF2]\nTraining model [PETR4][IMF3]\nTraining model [PETR4][IMF4]\nTraining model [PETR4][IMF5]\nTraining model [PETR4][IMF6]\nTraining model [VALE3][IMF1]\nTraining model [VALE3][IMF2]\nTraining model [VALE3][IMF3]\nTraining model [VALE3][IMF4]\nTraining model [VALE3][IMF5]\nTraining model [VALE3][IMF6]\nTraining model [VALE3][IMF7]\nTraining model [BOVA11][IMF1]\nTraining model [BOVA11][IMF2]\nTraining model [BOVA11][IMF3]\nTraining model [BOVA11][IMF4]\nTraining model [BOVA11][IMF5]\nTraining model [BOVA11][IMF6]\nTraining model [ITUB4][IMF1]\nTraining model [ITUB4][IMF2]\nTraining model [ITUB4][IMF3]\nTraining model [ITUB4][IMF4]\nTraining model [ITUB4][IMF5]\nTraining model [ITUB4][IMF6]\nTraining model [ITUB4][IMF7]\nTraining model [BBDC4][IMF1]\nTraining model [BBDC4][IMF2]\nTraining model [BBDC4][IMF3]\nTraining model [BBDC4][IMF4]\nTraining model [BBDC4][IMF5]\nTraining model [BBDC4][IMF6]\nTraining model [BBDC4][IMF7]\nTraining model [B3SA3][IMF1]\nTraining model [B3SA3][IMF2]\nTraining model [B3SA3][IMF3]\nTraining model [B3SA3][IMF4]\nTraining model [B3SA3][IMF5]\nTraining model [B3SA3][IMF6]\nTraining model [BBAS3][IMF1]\nTraining model [BBAS3][IMF2]\nTraining model [BBAS3][IMF3]\nTraining model [BBAS3][IMF4]\nTraining model [BBAS3][IMF5]\nTraining model [BBAS3][IMF6]\nTraining model [BBAS3][IMF7]\nTraining model [ABEV3][IMF1]\nTraining model [ABEV3][IMF2]\nTraining model [ABEV3][IMF3]\nTraining model [ABEV3][IMF4]\nTraining model [ABEV3][IMF5]\nTraining model [ABEV3][IMF6]\nTraining model [ABEV3][IMF7]\nTraining model [MGLU3][IMF1]\nTraining model [MGLU3][IMF2]\nTraining model [MGLU3][IMF3]\nTraining model [MGLU3][IMF4]\nTraining model [MGLU3][IMF5]\nTraining model [MGLU3][IMF6]\nTraining model [MGLU3][IMF7]\nTraining model [VVAR3][IMF1]\nTraining model [VVAR3][IMF2]\nTraining model [VVAR3][IMF3]\nTraining model [VVAR3][IMF4]\nTraining model [VVAR3][IMF5]\nTraining model [VVAR3][IMF6]\nTraining model [VVAR3][IMF7]\nCPU times: user 15min 11s, sys: 1min 57s, total: 17min 9s\nWall time: 11min 42s\n"
    }
   ],
   "source": [
    "%%time\n",
    "# Model Training\n",
    "\n",
    "models = {}\n",
    "\n",
    "model_epochs = {\n",
    "    'IMF1': 2500,\n",
    "    'IMF2': 2000,\n",
    "    'IMF3': 1500,\n",
    "    'IMF4': 1500,\n",
    "    'IMF5': 1500,\n",
    "    'IMF6': 1200,\n",
    "    'IMF7': 1200,\n",
    "    'IMF8': 1000,\n",
    "    'Rsd': 1000,\n",
    "    'DEFAULT': 1000\n",
    "}\n",
    "\n",
    "for ticker in train_generators:\n",
    "    models[ticker] = {}\n",
    "    reached_max_imf_of_target_feature = False\n",
    "    for imf_level in train_generators[ticker]:\n",
    "        print(f'Training model [{ticker}][{imf_level}]')\n",
    "        if reached_max_imf_of_target_feature is True:\n",
    "            break # no need to predict further if target feature doesn't contain greater IMF levels\n",
    "\n",
    "        if target_feature_max_imf_level[ticker] == imf_level:\n",
    "            reached_max_imf_of_target_feature = True\n",
    "        # Prediction model\n",
    "        model = Sequential()\n",
    "        current_dataset = train_dataset[ticker][imf_level]\n",
    "        n_features = current_dataset.shape[1]\n",
    "        cur_tmp_gen = train_generators[ticker][imf_level]\n",
    "\n",
    "        if imf_level in windows_sizes_for_imf_level:\n",
    "            window_size = windows_sizes_for_imf_level[imf_level]\n",
    "        else: \n",
    "            window_size = windows_sizes_for_imf_level['DEFAULT']\n",
    "\n",
    "        model.add(LSTM(128, activation='tanh', return_sequences=True, input_shape=(window_size, n_features)))\n",
    "        model.add(LSTM(64, activation='tanh', input_shape=(window_size, 128)))\n",
    "        model.add(Dense(16))\n",
    "        model.add(LeakyReLU())\n",
    "        model.add(Dense(4))\n",
    "        model.add(LeakyReLU())\n",
    "        model.add(Dense(1)) # 1 target feature only\n",
    "        model.compile(optimizer='adam', loss='mse')\n",
    "\n",
    "        number_of_epochs = model_epochs[imf_level]\n",
    "        # fit model\n",
    "        model.fit_generator(cur_tmp_gen, steps_per_epoch=1, epochs=number_of_epochs, verbose=0)\n",
    "\n",
    "        models[ticker][imf_level] = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Predicting: [PETR4][IMF1]\nPredicting: [PETR4][IMF2]\nPredicting: [PETR4][IMF3]\nPredicting: [PETR4][IMF4]\nPredicting: [PETR4][IMF5]\nPredicting: [VALE3][IMF1]\nPredicting: [VALE3][IMF2]\nPredicting: [VALE3][IMF3]\nPredicting: [VALE3][IMF4]\nPredicting: [VALE3][IMF5]\nPredicting: [VALE3][IMF6]\nPredicting: [BOVA11][IMF1]\nPredicting: [BOVA11][IMF2]\nPredicting: [BOVA11][IMF3]\nPredicting: [BOVA11][IMF4]\nPredicting: [BOVA11][IMF5]\nPredicting: [ITUB4][IMF1]\nPredicting: [ITUB4][IMF2]\nPredicting: [ITUB4][IMF3]\nPredicting: [ITUB4][IMF4]\nPredicting: [ITUB4][IMF5]\nPredicting: [ITUB4][IMF6]\nPredicting: [BBDC4][IMF1]\nPredicting: [BBDC4][IMF2]\nPredicting: [BBDC4][IMF3]\nPredicting: [BBDC4][IMF4]\nPredicting: [BBDC4][IMF5]\nPredicting: [BBDC4][IMF6]\nPredicting: [B3SA3][IMF1]\nPredicting: [B3SA3][IMF2]\nPredicting: [B3SA3][IMF3]\nPredicting: [B3SA3][IMF4]\nPredicting: [B3SA3][IMF5]\nPredicting: [B3SA3][IMF6]\nPredicting: [BBAS3][IMF1]\nPredicting: [BBAS3][IMF2]\nPredicting: [BBAS3][IMF3]\nPredicting: [BBAS3][IMF4]\nPredicting: [BBAS3][IMF5]\nPredicting: [BBAS3][IMF6]\nPredicting: [ABEV3][IMF1]\nPredicting: [ABEV3][IMF2]\nPredicting: [ABEV3][IMF3]\nPredicting: [ABEV3][IMF4]\nPredicting: [ABEV3][IMF5]\nPredicting: [ABEV3][IMF6]\nPredicting: [MGLU3][IMF1]\nPredicting: [MGLU3][IMF2]\nPredicting: [MGLU3][IMF3]\nPredicting: [MGLU3][IMF4]\nPredicting: [MGLU3][IMF5]\nPredicting: [MGLU3][IMF6]\nPredicting: [VVAR3][IMF1]\nPredicting: [VVAR3][IMF2]\nPredicting: [VVAR3][IMF3]\nPredicting: [VVAR3][IMF4]\nPredicting: [VVAR3][IMF5]\nPredicting: [VVAR3][IMF6]\n"
    }
   ],
   "source": [
    "# predicting\n",
    "\n",
    "results = {}\n",
    "\n",
    "for ticker in models:\n",
    "    results[ticker] = {}\n",
    "\n",
    "    # initializing results dicitionary\n",
    "    for feature in features_in_order:\n",
    "        if feature != target_feature:\n",
    "            continue\n",
    "        results[ticker][feature] = {}\n",
    "        for imf_level in models[ticker]:\n",
    "            results[ticker][feature][imf_level] = {\n",
    "                'real_train': [],\n",
    "                'predicted_train': [],\n",
    "                'real_validation': [],\n",
    "                'predicted_validation': [],\n",
    "                'real_test': [],\n",
    "                'predicted_test': [],\n",
    "                'x_axis_train': [],\n",
    "                'x_axis_validation': [],\n",
    "                'x_axis_test': []\n",
    "            }\n",
    "\n",
    "    for imf_level in models[ticker]:\n",
    "        model = models[ticker][imf_level]\n",
    "        \n",
    "        print(f'Predicting: [{ticker}][{imf_level}]')\n",
    "\n",
    "        cur_train_gen = train_generators[ticker][imf_level]\n",
    "        cur_validation_gen = validation_generators[ticker][imf_level]\n",
    "        cur_test_gen = test_generators[ticker][imf_level]\n",
    "\n",
    "        # predicting train\n",
    "        day_counter = 0\n",
    "        for i in range(len(cur_train_gen)):\n",
    "            x, y = cur_train_gen[i]\n",
    "            yhat = model.predict(x, verbose=0)\n",
    "\n",
    "            for j in range(yhat.shape[1]):\n",
    "                results[ticker][target_feature][imf_level]['real_train'] += [y[:,j][0]]\n",
    "                results[ticker][target_feature][imf_level]['predicted_train'] += [yhat[:,j][0]]\n",
    "                results[ticker][target_feature][imf_level]['x_axis_train'] += [day_counter]\n",
    "            day_counter += 1\n",
    "\n",
    "        # predicting validation\n",
    "        for i in range(len(cur_validation_gen)):\n",
    "            x, y = cur_validation_gen[i]\n",
    "            yhat = model.predict(x, verbose=0)\n",
    "\n",
    "            for j in range(yhat.shape[1]):\n",
    "                results[ticker][target_feature][imf_level]['real_validation'] += [y[:,j][0]]\n",
    "                results[ticker][target_feature][imf_level]['predicted_validation'] += [yhat[:,j][0]]\n",
    "                results[ticker][target_feature][imf_level]['x_axis_validation'] += [day_counter]\n",
    "            day_counter += 1\n",
    "\n",
    "        # predicting test\n",
    "        for i in range(len(cur_test_gen)):\n",
    "            x, y = cur_test_gen[i]\n",
    "            yhat = model.predict(x, verbose=0)\n",
    "\n",
    "            for j in range(yhat.shape[1]):\n",
    "                results[ticker][target_feature][imf_level]['real_test'] += [y[:,j][0]]\n",
    "                results[ticker][target_feature][imf_level]['predicted_test'] += [yhat[:,j][0]]\n",
    "                results[ticker][target_feature][imf_level]['x_axis_test'] += [day_counter]\n",
    "            day_counter += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# organizing imf prediction results, concatenating train, validation and test\n",
    "concatenated_results = {}\n",
    "\n",
    "for ticker in results:\n",
    "    concatenated_results[ticker] = {}\n",
    "    for feature in results[ticker]:\n",
    "        concatenated_results[ticker][feature] = {}\n",
    "        for imf_level in results[ticker][feature]:\n",
    "            \n",
    "            df_result = pd.DataFrame.from_dict(results[ticker][feature][imf_level], orient='index').T\n",
    "            df_train = df_result[['real_train','predicted_train','x_axis_train']].set_index('x_axis_train').dropna(axis=0)\n",
    "            df_train.index.name = 'x'\n",
    "            df_validation = df_result[['real_validation','predicted_validation','x_axis_validation']].set_index('x_axis_validation').dropna(axis=0)\n",
    "            df_validation.index.name = 'x'\n",
    "            df_test = df_result[['real_test','predicted_test','x_axis_test']].set_index('x_axis_test').dropna(axis=0)\n",
    "            df_test.index.name = 'x'\n",
    "\n",
    "            df_concatenated = pd.concat([df_train,df_validation,df_test], axis=1)\n",
    "\n",
    "            concatenated_results[ticker][feature][imf_level] = df_concatenated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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     "output_type": "display_data",
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     },
     "metadata": {}
    }
   ],
   "source": [
    "# plotting partial result\n",
    "plot_ticker = 'BOVA11'\n",
    "plot_feature = target_feature\n",
    "plot_imf = 'IMF2'\n",
    "\n",
    "concatenated_results[plot_ticker][plot_feature][plot_imf].iplot(title=f'{plot_ticker} {plot_feature} {plot_imf}', asFigure=True, layout=space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                     PETR4       VALE3       BOVA11       ITUB4       BBDC4  \\\nmse_test_IMF1     0.000517    0.001243     0.000341    0.000609    0.000311   \nmape_test_IMF1  150.021675  235.377300   123.024018  137.722500  157.975238   \nmse_test_IMF2     0.000046    0.000206     0.000170    0.000074    0.000057   \nmape_test_IMF2  130.493569  133.557919  1416.296018  154.495529  121.965381   \nmse_test_IMF3     0.000032    0.000077     0.000039    0.000048    0.000017   \nmape_test_IMF3   81.917635   60.000545   108.066788   42.725098  157.703812   \nmse_test_IMF4     0.000202    0.000141     0.000078    0.000089    0.000072   \nmape_test_IMF4   67.411841   38.872301    56.895235   66.000646   43.215804   \nmse_test_IMF5     0.000050    0.000212     0.000093    0.000739    0.001767   \nmape_test_IMF5    1.225919   26.878845     2.110289   28.870703   28.089814   \nmse_test_IMF6          NaN    0.000171          NaN    0.000022    0.002152   \nmape_test_IMF6         NaN    1.890042          NaN    0.748664    7.867029   \n\n                     B3SA3       BBAS3       ABEV3       MGLU3       VVAR3  \nmse_test_IMF1     0.000155    0.000283    0.000674    0.000061    0.000503  \nmape_test_IMF1  260.546592  245.519127  157.824109  105.438748  131.519845  \nmse_test_IMF2     0.000020    0.000055    0.000173    0.000019    0.000278  \nmape_test_IMF2  176.193167  155.715074  239.527652  254.170126  252.280292  \nmse_test_IMF3     0.000024    0.000025    0.000041    0.000011    0.000131  \nmape_test_IMF3   87.282991   94.766989  125.491600   41.862736  148.705477  \nmse_test_IMF4     0.000029    0.000090    0.000480    0.000019    0.000299  \nmape_test_IMF4  173.403199   46.801478   65.553704  128.835647  324.967955  \nmse_test_IMF5     0.000053    0.000235    0.000010    0.000003    0.000250  \nmape_test_IMF5   66.445281   34.639581  142.675750    5.780079   33.934546  \nmse_test_IMF6     0.000014    0.000030    0.000007    0.000010    0.000007  \nmape_test_IMF6    1.289105    0.784146    0.525981    1.168600    1.189885  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PETR4</th>\n      <th>VALE3</th>\n      <th>BOVA11</th>\n      <th>ITUB4</th>\n      <th>BBDC4</th>\n      <th>B3SA3</th>\n      <th>BBAS3</th>\n      <th>ABEV3</th>\n      <th>MGLU3</th>\n      <th>VVAR3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>mse_test_IMF1</th>\n      <td>0.000517</td>\n      <td>0.001243</td>\n      <td>0.000341</td>\n      <td>0.000609</td>\n      <td>0.000311</td>\n      <td>0.000155</td>\n      <td>0.000283</td>\n      <td>0.000674</td>\n      <td>0.000061</td>\n      <td>0.000503</td>\n    </tr>\n    <tr>\n      <th>mape_test_IMF1</th>\n      <td>150.021675</td>\n      <td>235.377300</td>\n      <td>123.024018</td>\n      <td>137.722500</td>\n      <td>157.975238</td>\n      <td>260.546592</td>\n      <td>245.519127</td>\n      <td>157.824109</td>\n      <td>105.438748</td>\n      <td>131.519845</td>\n    </tr>\n    <tr>\n      <th>mse_test_IMF2</th>\n      <td>0.000046</td>\n      <td>0.000206</td>\n      <td>0.000170</td>\n      <td>0.000074</td>\n      <td>0.000057</td>\n      <td>0.000020</td>\n      <td>0.000055</td>\n      <td>0.000173</td>\n      <td>0.000019</td>\n      <td>0.000278</td>\n    </tr>\n    <tr>\n      <th>mape_test_IMF2</th>\n      <td>130.493569</td>\n      <td>133.557919</td>\n      <td>1416.296018</td>\n      <td>154.495529</td>\n      <td>121.965381</td>\n      <td>176.193167</td>\n      <td>155.715074</td>\n      <td>239.527652</td>\n      <td>254.170126</td>\n      <td>252.280292</td>\n    </tr>\n    <tr>\n      <th>mse_test_IMF3</th>\n      <td>0.000032</td>\n      <td>0.000077</td>\n      <td>0.000039</td>\n      <td>0.000048</td>\n      <td>0.000017</td>\n      <td>0.000024</td>\n      <td>0.000025</td>\n      <td>0.000041</td>\n      <td>0.000011</td>\n      <td>0.000131</td>\n    </tr>\n    <tr>\n      <th>mape_test_IMF3</th>\n      <td>81.917635</td>\n      <td>60.000545</td>\n      <td>108.066788</td>\n      <td>42.725098</td>\n      <td>157.703812</td>\n      <td>87.282991</td>\n      <td>94.766989</td>\n      <td>125.491600</td>\n      <td>41.862736</td>\n      <td>148.705477</td>\n    </tr>\n    <tr>\n      <th>mse_test_IMF4</th>\n      <td>0.000202</td>\n      <td>0.000141</td>\n      <td>0.000078</td>\n      <td>0.000089</td>\n      <td>0.000072</td>\n      <td>0.000029</td>\n      <td>0.000090</td>\n      <td>0.000480</td>\n      <td>0.000019</td>\n      <td>0.000299</td>\n    </tr>\n    <tr>\n      <th>mape_test_IMF4</th>\n      <td>67.411841</td>\n      <td>38.872301</td>\n      <td>56.895235</td>\n      <td>66.000646</td>\n      <td>43.215804</td>\n      <td>173.403199</td>\n      <td>46.801478</td>\n      <td>65.553704</td>\n      <td>128.835647</td>\n      <td>324.967955</td>\n    </tr>\n    <tr>\n      <th>mse_test_IMF5</th>\n      <td>0.000050</td>\n      <td>0.000212</td>\n      <td>0.000093</td>\n      <td>0.000739</td>\n      <td>0.001767</td>\n      <td>0.000053</td>\n      <td>0.000235</td>\n      <td>0.000010</td>\n      <td>0.000003</td>\n      <td>0.000250</td>\n    </tr>\n    <tr>\n      <th>mape_test_IMF5</th>\n      <td>1.225919</td>\n      <td>26.878845</td>\n      <td>2.110289</td>\n      <td>28.870703</td>\n      <td>28.089814</td>\n      <td>66.445281</td>\n      <td>34.639581</td>\n      <td>142.675750</td>\n      <td>5.780079</td>\n      <td>33.934546</td>\n    </tr>\n    <tr>\n      <th>mse_test_IMF6</th>\n      <td>NaN</td>\n      <td>0.000171</td>\n      <td>NaN</td>\n      <td>0.000022</td>\n      <td>0.002152</td>\n      <td>0.000014</td>\n      <td>0.000030</td>\n      <td>0.000007</td>\n      <td>0.000010</td>\n      <td>0.000007</td>\n    </tr>\n    <tr>\n      <th>mape_test_IMF6</th>\n      <td>NaN</td>\n      <td>1.890042</td>\n      <td>NaN</td>\n      <td>0.748664</td>\n      <td>7.867029</td>\n      <td>1.289105</td>\n      <td>0.784146</td>\n      <td>0.525981</td>\n      <td>1.168600</td>\n      <td>1.189885</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "concatenated_results_copy = concatenated_results.copy()\n",
    "accuracies_per_imf_detailed = {}\n",
    "\n",
    "for ticker in concatenated_results_copy:\n",
    "    accuracies_per_imf_detailed[ticker] = {}\n",
    "    for feature in concatenated_results_copy[ticker]:\n",
    "        # we are predicting only Adj Close, so we don't need to specify which feature we are predicting here\n",
    "        # accuracies_per_imf_detailed[ticker][feature] = {}\n",
    "        for imf_level in concatenated_results_copy[ticker][feature]:\n",
    "            real_test = concatenated_results_copy[ticker][feature][imf_level]['real_test'].values\n",
    "            predicted_test = concatenated_results_copy[ticker][feature][imf_level]['predicted_test'].values\n",
    "            real_validation = concatenated_results_copy[ticker][feature][imf_level]['real_validation'].values\n",
    "            predicted_validation = concatenated_results_copy[ticker][feature][imf_level]['predicted_validation'].values\n",
    "            real_train = concatenated_results_copy[ticker][feature][imf_level]['real_train'].values\n",
    "            predicted_train = concatenated_results_copy[ticker][feature][imf_level]['predicted_train'].values\n",
    "\n",
    "            # removing offset nan\n",
    "            real_test = real_test[~np.isnan(real_test)]\n",
    "            predicted_test = predicted_test[~np.isnan(predicted_test)]\n",
    "            real_validation = real_validation[~np.isnan(real_validation)]\n",
    "            predicted_validation = predicted_validation[~np.isnan(predicted_validation)]\n",
    "            real_train = real_train[~np.isnan(real_train)]\n",
    "            predicted_train = predicted_train[~np.isnan(predicted_train)]\n",
    "\n",
    "            accuracies_per_imf_detailed[ticker][f'mse_test_{imf_level}'] = mean_squared_error(real_train,predicted_train)\n",
    "            accuracies_per_imf_detailed[ticker][f'mape_test_{imf_level}'] = np.mean(np.abs((real_train - predicted_train) / real_train)) * 100\n",
    "\n",
    "            # we sre only interested in the test accuracy here, so no need for specifying train and validation accuracies\n",
    "            # accuracies_per_imf_detailed[ticker] = {\n",
    "            #     'mse_train':mean_squared_error(real_test,predicted_test),\n",
    "            #     'mse_validation':mean_squared_error(real_validation,predicted_validation),\n",
    "            #     'mse_test':mean_squared_error(real_train,predicted_train),\n",
    "            #     'mape_train':np.mean(np.abs((real_test - predicted_test) / real_test)) * 100,\n",
    "            #     'mape_validation':np.mean(np.abs((real_validation - predicted_validation) / real_validation)) * 100,\n",
    "            #     'mape_test':np.mean(np.abs((real_train - predicted_train) / real_train)) * 100,\n",
    "            # }\n",
    "\n",
    "df_accuracies_by_imfs = pd.DataFrame.from_dict(accuracies_per_imf_detailed)\n",
    "df_accuracies_by_imfs.to_csv(f'exp_records_accuracy_per_imf/xlstm_ceemdan_full_per_imf_{experiment_time}.csv', sep=',', encoding='utf-8')\n",
    "df_accuracies_by_imfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [],
   "source": [
    "# recomposing prediction by arithmetically adding the IMF curves\n",
    "\n",
    "final_prediction_results = {}\n",
    "max_window_size = 10\n",
    "\n",
    "for ticker in concatenated_results:\n",
    "    final_prediction_results[ticker] = {}\n",
    "    for feature in concatenated_results[ticker]:\n",
    "        addition_train = None\n",
    "        addition_validation = None\n",
    "        addition_test = None\n",
    "\n",
    "        addition_real_train = None\n",
    "        addition_real_validation = None\n",
    "        addition_real_test = None\n",
    "\n",
    "        # recomposing predictions\n",
    "        for imf_level in concatenated_results[ticker][feature]:\n",
    "            # adding test\n",
    "            can_sum = True\n",
    "            if addition_test is None:\n",
    "                addition_test = concatenated_results[ticker][feature][imf_level]['predicted_test'].values\n",
    "            else:\n",
    "                np_array_to_be_added = concatenated_results[ticker][feature][imf_level]['predicted_test'].values\n",
    "                cur_length = addition_test.shape[0]\n",
    "                next_np_array_length = np_array_to_be_added.shape[0]\n",
    "                if cur_length < next_np_array_length:\n",
    "                    if next_np_array_length-cur_length < max_window_size:\n",
    "                        np_array_to_be_added = np_array_to_be_added[next_np_array_length-cur_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                else: \n",
    "                    if cur_length-next_np_array_length < max_window_size:\n",
    "                        addition_test = addition_test[cur_length-next_np_array_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                \n",
    "                if can_sum:\n",
    "                    addition_test = np.add(addition_test,np_array_to_be_added)\n",
    "\n",
    "        for imf_level in concatenated_results[ticker][feature]:\n",
    "            # adding train\n",
    "            can_sum = True\n",
    "            if addition_train is None:\n",
    "                addition_train = concatenated_results[ticker][feature][imf_level]['predicted_train'].values\n",
    "            else:\n",
    "                np_array_to_be_added = concatenated_results[ticker][feature][imf_level]['predicted_train'].values\n",
    "                cur_length = addition_train.shape[0]\n",
    "                next_np_array_length = np_array_to_be_added.shape[0]\n",
    "                if cur_length < next_np_array_length:\n",
    "                    if next_np_array_length-cur_length < max_window_size:\n",
    "                        np_array_to_be_added = np_array_to_be_added[next_np_array_length-cur_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                else: \n",
    "                    if cur_length-next_np_array_length < max_window_size:\n",
    "                        addition_train = addition_train[cur_length-next_np_array_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                \n",
    "                if can_sum:\n",
    "                    addition_train = np.add(addition_train,np_array_to_be_added)\n",
    "\n",
    "        for imf_level in concatenated_results[ticker][feature]:\n",
    "            # adding validation\n",
    "            can_sum = True\n",
    "            if addition_validation is None:\n",
    "                addition_validation = concatenated_results[ticker][feature][imf_level]['predicted_validation'].values\n",
    "            else:\n",
    "                np_array_to_be_added = concatenated_results[ticker][feature][imf_level]['predicted_validation'].values\n",
    "                cur_length = addition_validation.shape[0]\n",
    "                next_np_array_length = np_array_to_be_added.shape[0]\n",
    "                if cur_length < next_np_array_length:\n",
    "                    if next_np_array_length-cur_length < max_window_size:\n",
    "                        np_array_to_be_added = np_array_to_be_added[next_np_array_length-cur_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                else: \n",
    "                    if cur_length-next_np_array_length < max_window_size:\n",
    "                        addition_validation = addition_validation[cur_length-next_np_array_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                \n",
    "                if can_sum:\n",
    "                    addition_validation = np.add(addition_validation,np_array_to_be_added)\n",
    "\n",
    "        # recomposing real\n",
    "        for imf_level in concatenated_results[ticker][feature]:\n",
    "            # adding test\n",
    "            can_sum = True\n",
    "            if addition_real_test is None:\n",
    "                addition_real_test = concatenated_results[ticker][feature][imf_level]['real_test'].values\n",
    "            else:\n",
    "                np_array_to_be_added = concatenated_results[ticker][feature][imf_level]['real_test'].values\n",
    "                cur_length = addition_real_test.shape[0]\n",
    "                next_np_array_length = np_array_to_be_added.shape[0]\n",
    "                if cur_length < next_np_array_length:\n",
    "                    if next_np_array_length-cur_length < max_window_size:\n",
    "                        np_array_to_be_added = np_array_to_be_added[next_np_array_length-cur_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                else: \n",
    "                    if cur_length-next_np_array_length < max_window_size:\n",
    "                        addition_real_test = addition_real_test[cur_length-next_np_array_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                \n",
    "                if can_sum:\n",
    "                    addition_real_test = np.add(addition_real_test,np_array_to_be_added)\n",
    "\n",
    "        for imf_level in concatenated_results[ticker][feature]:\n",
    "            # adding train\n",
    "            can_sum = True\n",
    "            if addition_real_train is None:\n",
    "                addition_real_train = concatenated_results[ticker][feature][imf_level]['real_train'].values\n",
    "            else:\n",
    "                np_array_to_be_added = concatenated_results[ticker][feature][imf_level]['real_train'].values\n",
    "                cur_length = addition_real_train.shape[0]\n",
    "                next_np_array_length = np_array_to_be_added.shape[0]\n",
    "                if cur_length < next_np_array_length:\n",
    "                    if next_np_array_length-cur_length < max_window_size:\n",
    "                        np_array_to_be_added = np_array_to_be_added[next_np_array_length-cur_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                else: \n",
    "                    if cur_length-next_np_array_length < max_window_size:\n",
    "                        addition_real_train = addition_real_train[cur_length-next_np_array_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                \n",
    "                if can_sum:\n",
    "                    addition_real_train = np.add(addition_real_train,np_array_to_be_added)\n",
    "\n",
    "        for imf_level in concatenated_results[ticker][feature]:\n",
    "            # adding validation\n",
    "            can_sum = True\n",
    "            if addition_real_validation is None:\n",
    "                addition_real_validation = concatenated_results[ticker][feature][imf_level]['real_validation'].values\n",
    "            else:\n",
    "                np_array_to_be_added = concatenated_results[ticker][feature][imf_level]['real_validation'].values\n",
    "                cur_length = addition_real_validation.shape[0]\n",
    "                next_np_array_length = np_array_to_be_added.shape[0]\n",
    "                if cur_length < next_np_array_length:\n",
    "                    if next_np_array_length-cur_length < max_window_size:\n",
    "                        np_array_to_be_added = np_array_to_be_added[next_np_array_length-cur_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                else: \n",
    "                    if cur_length-next_np_array_length < max_window_size:\n",
    "                        addition_real_validation = addition_real_validation[cur_length-next_np_array_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                \n",
    "                if can_sum:\n",
    "                    addition_real_validation = np.add(addition_real_validation,np_array_to_be_added)\n",
    "        \n",
    "        scaler = scalers[ticker][feature]\n",
    "\n",
    "        final_prediction_results[ticker][feature] = {\n",
    "            'train_predicted': scaler.inverse_transform(addition_train.reshape(-1,1)).reshape(-1),\n",
    "            'validation_predicted': scaler.inverse_transform(addition_validation.reshape(-1,1)).reshape(-1),\n",
    "            'test_predicted': scaler.inverse_transform(addition_test.reshape(-1,1)).reshape(-1),\n",
    "            'train_real': scaler.inverse_transform(addition_real_train.reshape(-1,1)).reshape(-1),\n",
    "            'validation_real': scaler.inverse_transform(addition_real_validation.reshape(-1,1)).reshape(-1),\n",
    "            'test_real': scaler.inverse_transform(addition_real_test.reshape(-1,1)).reshape(-1)\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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     "output_type": "display_data",
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     "metadata": {}
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   ],
   "source": [
    "# plotting final result\n",
    "plot_ticker = 'PETR4'\n",
    "plot_feature = 'Adj Close'\n",
    "\n",
    "pd.DataFrame.from_dict(final_prediction_results[plot_ticker][plot_feature]).iplot(title=f'{plot_ticker} {plot_feature} {plot_imf}', layout=space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "             mse      mape\nPETR4   0.081667  1.081138\nVALE3   2.790892  1.354139\nBOVA11  1.100999  1.035557\nITUB4   0.941798  1.442776\nBBDC4   0.105555  3.351380\nB3SA3   0.246135  1.612269\nBBAS3   5.666842  3.552007\nABEV3   0.066316  1.239539\nMGLU3   1.254883  4.584050\nVVAR3   0.100088  3.060256",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>mse</th>\n      <th>mape</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>PETR4</th>\n      <td>0.081667</td>\n      <td>1.081138</td>\n    </tr>\n    <tr>\n      <th>VALE3</th>\n      <td>2.790892</td>\n      <td>1.354139</td>\n    </tr>\n    <tr>\n      <th>BOVA11</th>\n      <td>1.100999</td>\n      <td>1.035557</td>\n    </tr>\n    <tr>\n      <th>ITUB4</th>\n      <td>0.941798</td>\n      <td>1.442776</td>\n    </tr>\n    <tr>\n      <th>BBDC4</th>\n      <td>0.105555</td>\n      <td>3.351380</td>\n    </tr>\n    <tr>\n      <th>B3SA3</th>\n      <td>0.246135</td>\n      <td>1.612269</td>\n    </tr>\n    <tr>\n      <th>BBAS3</th>\n      <td>5.666842</td>\n      <td>3.552007</td>\n    </tr>\n    <tr>\n      <th>ABEV3</th>\n      <td>0.066316</td>\n      <td>1.239539</td>\n    </tr>\n    <tr>\n      <th>MGLU3</th>\n      <td>1.254883</td>\n      <td>4.584050</td>\n    </tr>\n    <tr>\n      <th>VVAR3</th>\n      <td>0.100088</td>\n      <td>3.060256</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "# calculating accuracy metrics\n",
    "\n",
    "adj_close_accuracies = {}\n",
    "accuracies_detailed = {}\n",
    "\n",
    "for ticker in final_prediction_results:\n",
    "    adj_close_accuracies[ticker] = {}\n",
    "    accuracies_detailed[ticker] = {}\n",
    "    for feature in final_prediction_results[ticker]:\n",
    "\n",
    "        y_train = final_prediction_results[ticker][feature]['train_predicted'][~np.isnan(final_prediction_results[ticker][feature]['train_predicted'])]\n",
    "        yhat_train = final_prediction_results[ticker][feature]['train_real'][~np.isnan(final_prediction_results[ticker][feature]['train_real'])]\n",
    "\n",
    "        y_validation = final_prediction_results[ticker][feature]['validation_predicted'][~np.isnan(final_prediction_results[ticker][feature]['validation_predicted'])]\n",
    "        yhat_validation = final_prediction_results[ticker][feature]['validation_real'][~np.isnan(final_prediction_results[ticker][feature]['validation_real'])]\n",
    "\n",
    "        y_test = final_prediction_results[ticker][feature]['test_predicted'][~np.isnan(final_prediction_results[ticker][feature]['test_predicted'])]\n",
    "        yhat_test = final_prediction_results[ticker][feature]['test_real'][~np.isnan(final_prediction_results[ticker][feature]['test_real'])]\n",
    "        accuracies_detailed[ticker][feature] = {\n",
    "            'mse':{\n",
    "                'train':mean_squared_error(y_train,yhat_train),\n",
    "                'validation':mean_squared_error(y_validation,yhat_validation),\n",
    "                'test':mean_squared_error(y_test,yhat_test),\n",
    "            },\n",
    "            'mape':{\n",
    "                'train':np.mean(np.abs((y_train - yhat_train) / y_train)) * 100,\n",
    "                'validation':np.mean(np.abs((y_validation - yhat_validation) / y_validation)) * 100,\n",
    "                'test':np.mean(np.abs((y_test - yhat_test) / y_test)) * 100,\n",
    "            }\n",
    "        }\n",
    "\n",
    "        if feature == 'Adj Close':\n",
    "            adj_close_accuracies[ticker] = {\n",
    "                'mse': mean_squared_error(y_test,yhat_test),\n",
    "                'mape': np.mean(np.abs((y_validation - yhat_validation) / y_validation)) * 100\n",
    "            }\n",
    "\n",
    "# pd.DataFrame.from_dict(accuracies_detailed[plot_ticker][plot_feature])\n",
    "df_close_accuracies = pd.DataFrame.from_dict(adj_close_accuracies).T\n",
    "df_close_accuracies.to_csv(f'exp_records/xlstm_ceemdan_full_{experiment_time}.csv', sep=',', encoding='utf-8')\n",
    "df_close_accuracies"
   ]
  }
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