SARIMAv2.ipynb

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    "colab": {
      "name": "SARIMAv1.ipynb",
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      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mswastik/forecasting/blob/main/SARIMAv2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGf7Q3xDM8in",
        "outputId": "3471c86a-13ea-4755-e9f1-908b9d37ed1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        }
      },
      "source": [
        "!pip install pmdarima"
      ],
      "execution_count": 1,
      "outputs": [
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          "text": [
            "Collecting pmdarima\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/be/62/725b3b6ae0e56c77534de5a8139322e7b863ca53fd5bd6bd3b7de87d0c20/pmdarima-1.7.1-cp36-cp36m-manylinux1_x86_64.whl (1.5MB)\n",
            "\u001b[K     |████████████████████████████████| 1.5MB 9.3MB/s \n",
            "\u001b[?25hCollecting Cython<0.29.18,>=0.29\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e7/d7/510ddef0248f3e1e91f9cc7e31c0f35f8954d0af92c5c3fd4c853e859ebe/Cython-0.29.17-cp36-cp36m-manylinux1_x86_64.whl (2.1MB)\n",
            "\u001b[K     |████████████████████████████████| 2.1MB 54.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (1.4.1)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (1.1.2)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (1.24.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (0.16.0)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (0.22.2.post1)\n",
            "Collecting statsmodels<0.12,>=0.11\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/cb/83/540fd83238a18abe6c2d280fa8e489ac5fcefa1f370f0ca1acd16ae1b860/statsmodels-0.11.1-cp36-cp36m-manylinux1_x86_64.whl (8.7MB)\n",
            "\u001b[K     |████████████████████████████████| 8.7MB 66.3MB/s \n",
            "\u001b[?25hCollecting setuptools<50.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c3/a9/5dc32465951cf4812e9e93b4ad2d314893c2fa6d5f66ce5c057af6e76d85/setuptools-49.6.0-py3-none-any.whl (803kB)\n",
            "\u001b[K     |████████████████████████████████| 808kB 67.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (1.18.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19->pmdarima) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19->pmdarima) (2018.9)\n",
            "Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.6/dist-packages (from statsmodels<0.12,>=0.11->pmdarima) (0.5.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas>=0.19->pmdarima) (1.15.0)\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Installing collected packages: Cython, statsmodels, setuptools, pmdarima\n",
            "  Found existing installation: Cython 0.29.21\n",
            "    Uninstalling Cython-0.29.21:\n",
            "      Successfully uninstalled Cython-0.29.21\n",
            "  Found existing installation: statsmodels 0.10.2\n",
            "    Uninstalling statsmodels-0.10.2:\n",
            "      Successfully uninstalled statsmodels-0.10.2\n",
            "  Found existing installation: setuptools 50.3.0\n",
            "    Uninstalling setuptools-50.3.0:\n",
            "      Successfully uninstalled setuptools-50.3.0\n",
            "Successfully installed Cython-0.29.17 pmdarima-1.7.1 setuptools-49.6.0 statsmodels-0.11.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "pip_warning": {
                "packages": [
                  "pkg_resources"
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          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N4AkWyqjMh78"
      },
      "source": [
        "import pmdarima as pmd\n",
        "import pandas as pd\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.tsa.stattools import acf,pacf,adfuller\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "import matplotlib.pyplot as plt\n",
        "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf\n",
        "from statsmodels.tools.eval_measures import rmse\n",
        "from sklearn.model_selection import TimeSeriesSplit,cross_val_score\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import numpy as np\n",
        "import warnings\n",
        "import plotly.express as px\n",
        "import cufflinks as cf\n",
        "import plotly.tools as tls"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0yFQ-v8mNRJn",
        "outputId": "53a0d960-745b-4940-8a37-4064529bf2c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        }
      },
      "source": [
        "!git clone -l -s https://github.com/mswastik/forecasting.git cloned-repo\n",
        "%cd cloned-repo"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'cloned-repo'...\n",
            "warning: --local is ignored\n",
            "remote: Enumerating objects: 39, done.\u001b[K\n",
            "remote: Counting objects: 100% (39/39), done.\u001b[K\n",
            "remote: Compressing objects: 100% (38/38), done.\u001b[K\n",
            "remote: Total 39 (delta 14), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (39/39), done.\n",
            "/content/cloned-repo\n"
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          "name": "stdout"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "jxVpi3-4Mh8D"
      },
      "source": [
        "warnings.filterwarnings(\"ignore\")\n",
        "df = pd.read_excel('Forecasting1.xlsx',sheet_name='SO',index_col='del date')\n",
        "df.rename({'agg':'agg1'},axis=1,inplace=True)\n",
        "df['dd']=df.index.copy()\n",
        "df['month'] = df['dd'].dt.year.astype('str')+\"-\"+df['dd'].dt.month.astype('str')\n",
        "df2=df.groupby(['month','agg1']).sum().copy()\n",
        "df2.drop(['wt','cs size'],inplace=True,axis=1)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EzgmqLbcAqCN"
      },
      "source": [
        "toot=df.loc[df.agg1=='Tmmthpkhti Limi Yih'].copy()\n",
        "toot=pd.DataFrame(toot['cases'])\n",
        "toot=toot.resample('M').sum()"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BePhWCdw81sW",
        "outputId": "3dac8063-744d-41f1-93bc-00193c784530",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "px.line(toot,y='cases',x=toot.index)"
      ],
      "execution_count": 6,
      "outputs": [
        {
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              "                            \n",
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              "\n",
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              "var notebookContainer = gd.closest('#notebook-container');\n",
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              "    x.observe(notebookContainer, {childList: true});\n",
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              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TCzOsxSRDwEf"
      },
      "source": [
        "toot=toot.loc[toot.cases<=80000]"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7O9V1_GWMh9f",
        "outputId": "d2b898ed-4933-4f14-837f-81da485264a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from pmdarima.arima import ndiffs\n",
        "ndiffs(toot, alpha=0.05, test='kpss', max_d=6)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAjUH1JvMh9o",
        "outputId": "462a6cd2-8c47-4926-a732-d394fcf8961b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        }
      },
      "source": [
        "def adf_test(timeseries):\n",
        "    #Perform Dickey-Fuller test:\n",
        "    print ('Results of Dickey-Fuller Test:')\n",
        "    dftest = adfuller(timeseries, autolag='AIC')\n",
        "    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
        "    for key,value in dftest[4].items():\n",
        "       dfoutput['Critical Value (%s)'%key] = value\n",
        "    print (dfoutput)\n",
        "adf_test(toot)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Results of Dickey-Fuller Test:\n",
            "Test Statistic                 -1.755483\n",
            "p-value                         0.402776\n",
            "#Lags Used                      6.000000\n",
            "Number of Observations Used    88.000000\n",
            "Critical Value (1%)            -3.506944\n",
            "Critical Value (5%)            -2.894990\n",
            "Critical Value (10%)           -2.584615\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOSA4VeMMCQL"
      },
      "source": [
        "toot = toot.diff(1)\n",
        "toot.dropna(inplace=True)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cRV7S70K4qwI",
        "outputId": "755913a2-ba09-4003-a77d-7a8c58447be5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        }
      },
      "source": [
        "toot"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cases</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>del date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2012-10-31</th>\n",
              "      <td>5143.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-30</th>\n",
              "      <td>8220.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-12-31</th>\n",
              "      <td>5631.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-31</th>\n",
              "      <td>20630.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-02-28</th>\n",
              "      <td>12529.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-31</th>\n",
              "      <td>52193.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-31</th>\n",
              "      <td>43866.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>25063.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-31</th>\n",
              "      <td>25487.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-30</th>\n",
              "      <td>1780.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>95 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              cases\n",
              "del date           \n",
              "2012-10-31   5143.0\n",
              "2012-11-30   8220.0\n",
              "2012-12-31   5631.0\n",
              "2013-01-31  20630.0\n",
              "2013-02-28  12529.0\n",
              "...             ...\n",
              "2020-07-31  52193.0\n",
              "2020-08-31  43866.0\n",
              "2020-09-30  25063.0\n",
              "2020-10-31  25487.0\n",
              "2020-11-30   1780.0\n",
              "\n",
              "[95 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lMBljqe_Jyhg"
      },
      "source": [
        "model1 = pmd.auto_arima(toot,seasonal=True,m=12)\n",
        "pred,ci=model1.predict_in_sample(toot[0:94],return_conf_int=True)\n",
        "pred1,ci1=model1.predict(10,return_conf_int=True)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "StQQxyV941kI",
        "outputId": "a4368418-6a1d-4dc3-fd0c-9bb1f50e4a68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "import plotly.graph_objects as go\n",
        "fig=px.line(data_frame=toot,x=toot.index,y='cases')\n",
        "fig.add_scatter(x=toot.index,y=pred,name='pred')\n",
        "fig.add_scatter(x=pd.date_range(toot.index[94], periods=10,freq='M'),y=pred1,name='pred1')\n",
        "fig.add_scatter(x=toot.index,y=ci[:,0])\n",
        "fig.add_scatter(x=toot.index,y=ci[:,1],fill='tonexty')\n",
        "fig.add_scatter(x=pd.date_range(toot.index[94], periods=10,freq='M'),y=ci1[:,0])\n",
        "fig.add_scatter(x=pd.date_range(toot.index[94], periods=10,freq='M'),y=ci1[:,1],fill='tonexty')"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
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      "source": [
        "pmd.arima.decompose(toot['cases'],type_='additive',m=12)"
      ],
      "execution_count": 74,
      "outputs": [
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              "decomposed(x=del date\n",
              "2012-10-31     5143.0\n",
              "2012-11-30     8220.0\n",
              "2012-12-31     5631.0\n",
              "2013-01-31    20630.0\n",
              "2013-02-28    12529.0\n",
              "               ...   \n",
              "2020-07-31    52193.0\n",
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              "2020-09-30    25063.0\n",
              "2020-10-31    25487.0\n",
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              "Name: cases, Length: 95, dtype: float64, trend=[nan, nan, nan, nan, nan, nan, 10646.916666666666, 11076.25, 10944.25, 12053.0, 11921.0, 11297.333333333332, 10645.916666666666, 11396.75, 12009.75, 11785.916666666666, 12238.166666666666, 13437.583333333332, 13731.166666666666, 13767.33333333333, 14489.499999999996, 13465.999999999998, 12905.749999999998, 13488.916666666664, 13110.416666666666, 12855.25, 12124.25, 12149.833333333334, 11452.25, 10732.583333333332, 10118.416666666666, 10473.166666666666, 10485.25, 10726.25, 11062.666666666666, 11437.583333333334, 12056.750000000002, 11747.583333333334, 11539.222166666666, 11405.638833333334, 11372.5555, 11480.5555, 12127.138833333334, 12082.5555, 11846.722166666666, 11767.38883333333, 11294.972166666665, 10619.88883333333, 11036.388833333333, 10742.888833333334, 11046.166666666666, 14150.5, 15183.416666666666, 15904.08333333333, 17934.666666666664, 22404.666666666664, 26650.583333333332, 29059.999999999993, 32431.16666666666, 34842.25, 39142.333333333336, 42507.99999999999, 45848.75, 46885.916666666664, 46562.74999999999, 47320.333333333336, 46955.916666666664, 47923.83333333333, 46178.83333333333, 48158.166666666664, 47653.416666666664, 48574.416666666664, 47332.75, 43715.5, 39947.08333333333, 36564.0, 36730.166666666664, 35255.75, 33279.75, 30421.916666666664, 31361.749999999993, 27881.83333333333, 25757.333333333332, 21901.0, 22529.75, 26794.916666666664, 29743.08333333333, 30655.66666666666, 31531.166666666664, nan, nan, nan, nan, nan, nan], seasonal=array([ 12545.09921429,   7191.87302381,    -37.47221429,   1521.36111905,\n",
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              "        12545.09921429,   7191.87302381,    -37.47221429,   1521.36111905,\n",
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              "        -1992.55554762, -11127.66269048,   1595.09921429,  -2578.05554762,\n",
              "        12545.09921429,   7191.87302381,    -37.47221429,   1521.36111905,\n",
              "        -5007.06745238,   3872.18519444,  -8126.13888095,  -2103.00802381,\n",
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              "        -1992.55554762, -11127.66269048,   1595.09921429]), random=del date\n",
              "2012-10-31   NaN\n",
              "2012-11-30   NaN\n",
              "2012-12-31   NaN\n",
              "2013-01-31   NaN\n",
              "2013-02-28   NaN\n",
              "              ..\n",
              "2020-07-31   NaN\n",
              "2020-08-31   NaN\n",
              "2020-09-30   NaN\n",
              "2020-10-31   NaN\n",
              "2020-11-30   NaN\n",
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      "execution_count": 79,
      "outputs": [
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              "      <td>...</td>\n",
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              "      <th>2020-07-31</th>\n",
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              "      <th>2020-08-31</th>\n",
              "      <td>43866.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>25063.0</td>\n",
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              "      <th>2020-10-31</th>\n",
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              "      <th>2020-11-30</th>\n",
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              "              cases\n",
              "del date           \n",
              "2012-10-31   5143.0\n",
              "2012-11-30   8220.0\n",
              "2012-12-31   5631.0\n",
              "2013-01-31  20630.0\n",
              "2013-02-28  12529.0\n",
              "...             ...\n",
              "2020-07-31  52193.0\n",
              "2020-08-31  43866.0\n",
              "2020-09-30  25063.0\n",
              "2020-10-31  25487.0\n",
              "2020-11-30   1780.0\n",
              "\n",
              "[95 rows x 1 columns]"
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          "base_uri": "https://localhost:8080/",
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      "source": [
        "#plt.figure(figsize=(15,10))\n",
        "#from plotly.plotly import plot_mpl\n",
        "hh=seasonal_decompose(x=toot,model='additive',freq=12).plot()\n",
        "hh.set_size_inches(15,13)\n",
        "tls.mpl_to_plotly(hh)"
      ],
      "execution_count": 81,
      "outputs": [
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              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('0c98b900-d21d-4e83-bfc4-6f94256a0615');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
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              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7f1uVyDJ5bDB"
      },
      "source": [
        "dd=pmd.arima.decompose(toot['cases'],type_='additive',m=12)\n",
        "pmd.utils.decomposed_plot(dd)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I3oVx_K2Mh8l"
      },
      "source": [
        "n_train = 24\n",
        "n_records = len(toot)\n",
        "fore = {}\n",
        "res=[]\n",
        "for i in range(n_train, n_records):\n",
        "    pre=[]\n",
        "    train, test = toot[0:i].copy(), toot[i:i+1].copy()\n",
        "    mod = SARIMAX(train, order=(0, 1, 1), seasonal_order=(2, 0, 0, 12))\n",
        "    mod=mod.fit()\n",
        "    fore.update(mod.forecast(1))\n",
        "    pre = mod.forecast()\n",
        "    res.extend(rmse(test,pre))\n",
        "#df1.plot(legend=True,figsize=(15,10))\n",
        "#pre.plot(legend=True)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKn9DJf9Mh8r",
        "outputId": "587beb24-06b5-407a-d12c-6e69d2013c63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"Avg rmse is %d\" %np.mean(res))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Avg rmse is 12991\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ZzPXANEDv1b"
      },
      "source": [
        "mod1 = SARIMAX(toot, order=(0, 1, 1), seasonal_order=(2, 0, 0, 3))\n",
        "mod1 = mod1.fit()"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h9H8IerLSw5K",
        "outputId": "0542dc5c-c550-4a53-c46f-7f687d42fc70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        }
      },
      "source": [
        "predict=mod1.get_prediction(12)\n",
        "pred=predict.conf_int()\n",
        "pred"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>lower cases</th>\n",
              "      <th>upper cases</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>del date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-11-30</th>\n",
              "      <td>-44190.426570</td>\n",
              "      <td>35343.551762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-12-31</th>\n",
              "      <td>-37014.820467</td>\n",
              "      <td>42142.854471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-31</th>\n",
              "      <td>-37630.434059</td>\n",
              "      <td>41217.754926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-02-28</th>\n",
              "      <td>-39426.864793</td>\n",
              "      <td>39162.532923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-03-31</th>\n",
              "      <td>-41542.946418</td>\n",
              "      <td>36827.033017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-31</th>\n",
              "      <td>-50982.086899</td>\n",
              "      <td>25366.755939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-31</th>\n",
              "      <td>-36568.760639</td>\n",
              "      <td>39778.403034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>-37472.154512</td>\n",
              "      <td>38873.405084</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-31</th>\n",
              "      <td>-36168.421023</td>\n",
              "      <td>40175.606095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-30</th>\n",
              "      <td>-29100.083984</td>\n",
              "      <td>47242.478940</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>82 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             lower cases   upper cases\n",
              "del date                              \n",
              "2013-11-30 -44190.426570  35343.551762\n",
              "2013-12-31 -37014.820467  42142.854471\n",
              "2014-01-31 -37630.434059  41217.754926\n",
              "2014-02-28 -39426.864793  39162.532923\n",
              "2014-03-31 -41542.946418  36827.033017\n",
              "...                  ...           ...\n",
              "2020-07-31 -50982.086899  25366.755939\n",
              "2020-08-31 -36568.760639  39778.403034\n",
              "2020-09-30 -37472.154512  38873.405084\n",
              "2020-10-31 -36168.421023  40175.606095\n",
              "2020-11-30 -29100.083984  47242.478940\n",
              "\n",
              "[82 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hx9njPmOMh9H",
        "outputId": "ea6d6a93-f5fe-4b36-d1f4-739cca0d6704",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "import plotly.graph_objects as go\n",
        "fig=px.line(data_frame=toot,x=toot.index,y='cases',labels=\"Actual\")\n",
        "fig.add_scatter(x=pred.index,y=pred['lower cases'])\n",
        "fig.add_scatter(x=pred.index,y=pred['upper cases'],fill='tonexty')\n",
        "fig.add_scatter(x=toot.index,y=toot['cases'])\n",
        "#fig2=px.area(cint)\n",
        "#fig.add_trace()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "            <script type=\"text/javascript\">\n",
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              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('5d3a0d9c-3cfb-43d0-8c5a-d119918b1d6b');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AYU-Wp4bMh9K"
      },
      "source": [
        "fig2=px.area(cint,x=cint.index,y='lower y')\n",
        "fig2.add_scatter(x=cint.index,y=cint['upper y'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OD9iEvJMh9Z",
        "outputId": "2fc65bdd-0a65-46fe-b52a-99c4db5e8a64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        }
      },
      "source": [
        "kk=mod1.plot_diagnostics()\n",
        "kk.set_size_inches(15,13)\n",
        "#tls.mpl_to_plotly(kk)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x936 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oLUfGE_ZMh8y",
        "outputId": "28fbc31c-a40b-4548-a313-8154cb07ff7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "mod.get_forecast(6).conf_int()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>lower cases</th>\n",
              "      <th>upper cases</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>-15699.988162</td>\n",
              "      <td>41292.435638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>-46756.966589</td>\n",
              "      <td>16665.465272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>-29071.925267</td>\n",
              "      <td>34350.506594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>-33558.716445</td>\n",
              "      <td>32006.382284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>-21968.040161</td>\n",
              "      <td>44097.556789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>-33953.215027</td>\n",
              "      <td>32112.381924</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     lower cases   upper cases\n",
              "93 -15699.988162  41292.435638\n",
              "94 -46756.966589  16665.465272\n",
              "95 -29071.925267  34350.506594\n",
              "96 -33558.716445  32006.382284\n",
              "97 -21968.040161  44097.556789\n",
              "98 -33953.215027  32112.381924"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H5y9FkeXMh8u"
      },
      "source": [
        "ff = pd.DataFrame.from_dict(data=fore,orient='index',columns=[\"pred\"])\n",
        "#pre1 = inv_boxcox(mod.get_forecast(6).predicted_mean,fitted_lambda)-1\n",
        "#pre1=pd.DataFrame(pre1)\n",
        "#pre1.rename(columns={0:\"pred\"},inplace=True)\n",
        "#ff=ff.append(pre1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txr3qwomMh8X",
        "outputId": "ceea6e92-225b-4022-e11e-a22b74ca228d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        }
      },
      "source": [
        "from scipy.stats import boxcox\n",
        "from scipy.special import inv_boxcox\n",
        "df3,fitted_lambda= boxcox(df1['Qty']+1,lmbda=None)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-34-fc86d63e5534>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mboxcox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspecial\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0minv_boxcox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfitted_lambda\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mboxcox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Qty'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlmbda\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'df1' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qSa7iF9HMh8H"
      },
      "source": [
        "def evaluate_arima_model(X, arima_order,s_order):\n",
        "    splits = TimeSeriesSplit(n_splits=3)\n",
        "    error=[]\n",
        "    # make predictions\n",
        "    for train_index, test_index in splits.split(X):\n",
        "        train = X.values[train_index]\n",
        "        test = X.values[test_index]\n",
        "        model = SARIMAX(train, order=arima_order,seasonal_order=s_order)\n",
        "        model_fit = model.fit(disp=0)\n",
        "        yhat = model_fit.predict(start=len(train), end=len(train)+len(test)-1, dynamic=False)\n",
        "        error.append(rmse(test.T[0], yhat))\n",
        "    return np.asarray(error).mean()\n",
        " \n",
        "# evaluate combinations of p, d and q values for an ARIMA model\n",
        "def evaluate_models(dataset, p_values, d_values, q_values, m):\n",
        "    dataset = dataset.astype('float32')\n",
        "    best_score, best_cfg = float(\"inf\"), None\n",
        "    for p in p_values:\n",
        "        for d in d_values:\n",
        "            for q in q_values:\n",
        "                for P in p_values:\n",
        "                    for D in d_values:\n",
        "                        for Q in q_values:\n",
        "                            order = (p,d,q)\n",
        "                            s_order = (P,D,Q,m)\n",
        "                            try:\n",
        "                                mse = evaluate_arima_model(dataset, order,s_order)\n",
        "                                if mse < best_score:\n",
        "                                    best_score, best_cfg = mse, (order,s_order)\n",
        "                                print('SARIMA%sx%s RMSE=%.3f' % (order,s_order,mse))\n",
        "                            except:\n",
        "                                continue\n",
        "    print('Best SARIMA %s RMSE=%.3f' % (best_cfg, best_score))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "id": "bTPPRkBDMh8P",
        "outputId": "c0b1e770-7a12-423f-f8c1-6996c6943ffb"
      },
      "source": [
        "p_values = [0,1,2,3,4]\n",
        "d_values = range(0, 2)\n",
        "q_values = range(0, 4)\n",
        "evaluate_models(df1, p_values, d_values, q_values,12)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SARIMA(0, 0, 0)x(0, 0, 0, 12) RMSE=1716.003\n",
            "SARIMA(0, 0, 0)x(0, 0, 1, 12) RMSE=1554.912\n",
            "SARIMA(0, 0, 0)x(0, 0, 2, 12) RMSE=1437.645\n",
            "SARIMA(0, 0, 0)x(0, 0, 3, 12) RMSE=1374.687\n",
            "SARIMA(0, 0, 0)x(0, 1, 0, 12) RMSE=1177.831\n",
            "SARIMA(0, 0, 0)x(0, 1, 1, 12) RMSE=1152.103\n",
            "SARIMA(0, 0, 0)x(0, 1, 2, 12) RMSE=1182.464\n",
            "SARIMA(0, 0, 0)x(0, 1, 3, 12) RMSE=1179.829\n",
            "SARIMA(0, 0, 0)x(1, 0, 0, 12) RMSE=1241.914\n",
            "SARIMA(0, 0, 0)x(1, 0, 1, 12) RMSE=1274.391\n",
            "SARIMA(0, 0, 0)x(1, 0, 2, 12) RMSE=1312.642\n",
            "SARIMA(0, 0, 0)x(1, 0, 3, 12) RMSE=1308.297\n",
            "SARIMA(0, 0, 0)x(1, 1, 0, 12) RMSE=1181.554\n",
            "SARIMA(0, 0, 0)x(1, 1, 1, 12) RMSE=1197.323\n",
            "SARIMA(0, 0, 0)x(1, 1, 2, 12) RMSE=1199.893\n",
            "SARIMA(0, 0, 0)x(1, 1, 3, 12) RMSE=1179.862\n",
            "SARIMA(0, 0, 0)x(2, 0, 0, 12) RMSE=1341.675\n",
            "SARIMA(0, 0, 0)x(2, 0, 1, 12) RMSE=1317.626\n",
            "SARIMA(0, 0, 0)x(2, 0, 2, 12) RMSE=1327.595\n",
            "SARIMA(0, 0, 0)x(2, 0, 3, 12) RMSE=1325.177\n",
            "SARIMA(0, 0, 0)x(2, 1, 0, 12) RMSE=1192.402\n",
            "SARIMA(0, 0, 0)x(2, 1, 1, 12) RMSE=1184.396\n",
            "SARIMA(0, 0, 0)x(2, 1, 2, 12) RMSE=1193.890\n",
            "SARIMA(0, 0, 0)x(2, 1, 3, 12) RMSE=1187.288\n",
            "SARIMA(0, 0, 0)x(3, 0, 0, 12) RMSE=1336.441\n",
            "SARIMA(0, 0, 0)x(3, 0, 1, 12) RMSE=1316.053\n",
            "SARIMA(0, 0, 0)x(3, 0, 2, 12) RMSE=1325.511\n",
            "SARIMA(0, 0, 0)x(3, 0, 3, 12) RMSE=1319.261\n",
            "SARIMA(0, 0, 0)x(3, 1, 0, 12) RMSE=1190.112\n",
            "SARIMA(0, 0, 0)x(3, 1, 1, 12) RMSE=1186.491\n",
            "SARIMA(0, 0, 0)x(3, 1, 2, 12) RMSE=1184.977\n",
            "SARIMA(0, 0, 0)x(3, 1, 3, 12) RMSE=1192.787\n",
            "SARIMA(0, 0, 0)x(4, 0, 0, 12) RMSE=1318.575\n",
            "SARIMA(0, 0, 0)x(4, 0, 1, 12) RMSE=1315.740\n",
            "SARIMA(0, 0, 0)x(4, 0, 2, 12) RMSE=1318.919\n",
            "SARIMA(0, 0, 0)x(4, 0, 3, 12) RMSE=1316.773\n",
            "SARIMA(0, 0, 0)x(4, 1, 0, 12) RMSE=1189.137\n",
            "SARIMA(0, 0, 0)x(4, 1, 1, 12) RMSE=1190.255\n",
            "SARIMA(0, 0, 0)x(4, 1, 2, 12) RMSE=1195.563\n",
            "SARIMA(0, 0, 0)x(4, 1, 3, 12) RMSE=1181.864\n",
            "SARIMA(0, 0, 1)x(0, 0, 0, 12) RMSE=1721.961\n",
            "SARIMA(0, 0, 1)x(0, 0, 1, 12) RMSE=1616.947\n",
            "SARIMA(0, 0, 1)x(0, 0, 2, 12) RMSE=1498.164\n",
            "SARIMA(0, 0, 1)x(0, 0, 3, 12) RMSE=1388.356\n",
            "SARIMA(0, 0, 1)x(0, 1, 0, 12) RMSE=1175.561\n",
            "SARIMA(0, 0, 1)x(0, 1, 1, 12) RMSE=1143.423\n",
            "SARIMA(0, 0, 1)x(0, 1, 2, 12) RMSE=1176.175\n",
            "SARIMA(0, 0, 1)x(0, 1, 3, 12) RMSE=1176.209\n",
            "SARIMA(0, 0, 1)x(1, 0, 0, 12) RMSE=1347.772\n",
            "SARIMA(0, 0, 1)x(1, 0, 1, 12) RMSE=1292.469\n",
            "SARIMA(0, 0, 1)x(1, 0, 2, 12) RMSE=1362.124\n",
            "SARIMA(0, 0, 1)x(1, 0, 3, 12) RMSE=1290.434\n",
            "SARIMA(0, 0, 1)x(1, 1, 0, 12) RMSE=1177.716\n",
            "SARIMA(0, 0, 1)x(1, 1, 1, 12) RMSE=1194.619\n",
            "SARIMA(0, 0, 1)x(1, 1, 2, 12) RMSE=1180.339\n",
            "SARIMA(0, 0, 1)x(1, 1, 3, 12) RMSE=1173.629\n",
            "SARIMA(0, 0, 1)x(2, 0, 0, 12) RMSE=1326.310\n",
            "SARIMA(0, 0, 1)x(2, 0, 1, 12) RMSE=1346.621\n",
            "SARIMA(0, 0, 1)x(2, 0, 2, 12) RMSE=1268.590\n",
            "SARIMA(0, 0, 1)x(2, 0, 3, 12) RMSE=1345.659\n",
            "SARIMA(0, 0, 1)x(2, 1, 0, 12) RMSE=1192.478\n",
            "SARIMA(0, 0, 1)x(2, 1, 1, 12) RMSE=1179.206\n",
            "SARIMA(0, 0, 1)x(2, 1, 2, 12) RMSE=1189.157\n",
            "SARIMA(0, 0, 1)x(2, 1, 3, 12) RMSE=1186.909\n",
            "SARIMA(0, 0, 1)x(3, 0, 0, 12) RMSE=1306.239\n",
            "SARIMA(0, 0, 1)x(3, 0, 1, 12) RMSE=1355.810\n",
            "SARIMA(0, 0, 1)x(3, 0, 2, 12) RMSE=1326.335\n",
            "SARIMA(0, 0, 1)x(3, 1, 0, 12) RMSE=1187.780\n",
            "SARIMA(0, 0, 1)x(3, 1, 1, 12) RMSE=1191.983\n",
            "SARIMA(0, 0, 1)x(3, 1, 2, 12) RMSE=1178.432\n",
            "SARIMA(0, 0, 1)x(3, 1, 3, 12) RMSE=1172.770\n",
            "SARIMA(0, 0, 1)x(4, 0, 0, 12) RMSE=1300.243\n",
            "SARIMA(0, 0, 1)x(4, 0, 1, 12) RMSE=1358.638\n",
            "SARIMA(0, 0, 1)x(4, 1, 0, 12) RMSE=1186.535\n",
            "SARIMA(0, 0, 1)x(4, 1, 1, 12) RMSE=1187.241\n",
            "SARIMA(0, 0, 1)x(4, 1, 2, 12) RMSE=1189.351\n",
            "SARIMA(0, 0, 1)x(4, 1, 3, 12) RMSE=1184.087\n",
            "SARIMA(0, 0, 2)x(0, 0, 0, 12) RMSE=1691.363\n",
            "SARIMA(0, 0, 2)x(0, 0, 1, 12) RMSE=1590.711\n",
            "SARIMA(0, 0, 2)x(0, 0, 2, 12) RMSE=1476.357\n",
            "SARIMA(0, 0, 2)x(0, 0, 3, 12) RMSE=1448.355\n",
            "SARIMA(0, 0, 2)x(0, 1, 0, 12) RMSE=1202.166\n",
            "SARIMA(0, 0, 2)x(0, 1, 1, 12) RMSE=1147.118\n",
            "SARIMA(0, 0, 2)x(0, 1, 2, 12) RMSE=1164.594\n",
            "SARIMA(0, 0, 2)x(0, 1, 3, 12) RMSE=1164.088\n",
            "SARIMA(0, 0, 2)x(1, 0, 0, 12) RMSE=1344.898\n",
            "SARIMA(0, 0, 2)x(1, 0, 1, 12) RMSE=1300.047\n",
            "SARIMA(0, 0, 2)x(1, 0, 2, 12) RMSE=1334.629\n",
            "SARIMA(0, 0, 2)x(1, 0, 3, 12) RMSE=1411.713\n",
            "SARIMA(0, 0, 2)x(1, 1, 0, 12) RMSE=1223.034\n",
            "SARIMA(0, 0, 2)x(1, 1, 1, 12) RMSE=1184.374\n",
            "SARIMA(0, 0, 2)x(1, 1, 2, 12) RMSE=1175.028\n",
            "SARIMA(0, 0, 2)x(1, 1, 3, 12) RMSE=1193.163\n",
            "SARIMA(0, 0, 2)x(2, 0, 0, 12) RMSE=1423.720\n",
            "SARIMA(0, 0, 2)x(2, 0, 1, 12) RMSE=1291.729\n",
            "SARIMA(0, 0, 2)x(2, 0, 2, 12) RMSE=1420.923\n",
            "SARIMA(0, 0, 2)x(2, 1, 0, 12) RMSE=1219.170\n",
            "SARIMA(0, 0, 2)x(2, 1, 1, 12) RMSE=1184.622\n",
            "SARIMA(0, 0, 2)x(2, 1, 2, 12) RMSE=1225.659\n",
            "SARIMA(0, 0, 2)x(2, 1, 3, 12) RMSE=1152.443\n",
            "SARIMA(0, 0, 2)x(3, 0, 0, 12) RMSE=1418.224\n",
            "SARIMA(0, 0, 2)x(3, 0, 1, 12) RMSE=1406.500\n",
            "SARIMA(0, 0, 2)x(3, 0, 2, 12) RMSE=1411.541\n",
            "SARIMA(0, 0, 2)x(3, 1, 0, 12) RMSE=1218.036\n",
            "SARIMA(0, 0, 2)x(3, 1, 1, 12) RMSE=1188.516\n",
            "SARIMA(0, 0, 2)x(3, 1, 2, 12) RMSE=1177.195\n",
            "SARIMA(0, 0, 2)x(3, 1, 3, 12) RMSE=1188.794\n",
            "SARIMA(0, 0, 2)x(4, 0, 0, 12) RMSE=1420.445\n",
            "SARIMA(0, 0, 2)x(4, 0, 1, 12) RMSE=1402.759\n",
            "SARIMA(0, 0, 2)x(4, 0, 3, 12) RMSE=1413.681\n",
            "SARIMA(0, 0, 2)x(4, 1, 0, 12) RMSE=1211.117\n",
            "SARIMA(0, 0, 2)x(4, 1, 1, 12) RMSE=1190.607\n",
            "SARIMA(0, 0, 2)x(4, 1, 2, 12) RMSE=1182.423\n",
            "SARIMA(0, 0, 2)x(4, 1, 3, 12) RMSE=1207.066\n",
            "SARIMA(0, 0, 3)x(0, 0, 0, 12) RMSE=1684.129\n",
            "SARIMA(0, 0, 3)x(0, 0, 1, 12) RMSE=1619.301\n",
            "SARIMA(0, 0, 3)x(0, 0, 2, 12) RMSE=1473.473\n",
            "SARIMA(0, 0, 3)x(0, 0, 3, 12) RMSE=1440.313\n",
            "SARIMA(0, 0, 3)x(0, 1, 0, 12) RMSE=nan\n",
            "SARIMA(0, 0, 3)x(0, 1, 1, 12) RMSE=nan\n",
            "SARIMA(0, 0, 3)x(0, 1, 2, 12) RMSE=nan\n",
            "SARIMA(0, 0, 3)x(0, 1, 3, 12) RMSE=nan\n",
            "SARIMA(0, 0, 3)x(1, 0, 0, 12) RMSE=1455.885\n",
            "SARIMA(0, 0, 3)x(1, 0, 1, 12) RMSE=1314.275\n",
            "SARIMA(0, 0, 3)x(1, 0, 2, 12) RMSE=1405.168\n",
            "SARIMA(0, 0, 3)x(1, 0, 3, 12) RMSE=1413.995\n",
            "SARIMA(0, 0, 3)x(2, 0, 0, 12) RMSE=1341.422\n",
            "SARIMA(0, 0, 3)x(2, 0, 1, 12) RMSE=1330.703\n",
            "SARIMA(0, 0, 3)x(2, 0, 3, 12) RMSE=1403.942\n",
            "SARIMA(0, 0, 3)x(3, 0, 0, 12) RMSE=1340.984\n",
            "SARIMA(0, 0, 3)x(3, 0, 1, 12) RMSE=1353.229\n",
            "SARIMA(0, 0, 3)x(3, 0, 3, 12) RMSE=1454.456\n",
            "SARIMA(0, 0, 3)x(4, 0, 0, 12) RMSE=1382.651\n",
            "SARIMA(0, 0, 3)x(4, 0, 1, 12) RMSE=1400.200\n",
            "SARIMA(0, 0, 3)x(4, 0, 2, 12) RMSE=1335.649\n",
            "SARIMA(0, 1, 0)x(0, 0, 0, 12) RMSE=1452.097\n",
            "SARIMA(0, 1, 0)x(0, 0, 1, 12) RMSE=1449.730\n",
            "SARIMA(0, 1, 0)x(0, 0, 2, 12) RMSE=1450.755\n",
            "SARIMA(0, 1, 0)x(0, 0, 3, 12) RMSE=1430.507\n",
            "SARIMA(0, 1, 0)x(0, 1, 0, 12) RMSE=2001.637\n",
            "SARIMA(0, 1, 0)x(0, 1, 1, 12) RMSE=1608.526\n",
            "SARIMA(0, 1, 0)x(0, 1, 2, 12) RMSE=1523.994\n",
            "SARIMA(0, 1, 0)x(0, 1, 3, 12) RMSE=1462.658\n",
            "SARIMA(0, 1, 0)x(1, 0, 0, 12) RMSE=1447.918\n",
            "SARIMA(0, 1, 0)x(1, 0, 1, 12) RMSE=1472.311\n",
            "SARIMA(0, 1, 0)x(1, 0, 2, 12) RMSE=1443.137\n",
            "SARIMA(0, 1, 0)x(1, 0, 3, 12) RMSE=1423.106\n",
            "SARIMA(0, 1, 0)x(1, 1, 0, 12) RMSE=1488.273\n",
            "SARIMA(0, 1, 0)x(1, 1, 1, 12) RMSE=1370.016\n",
            "SARIMA(0, 1, 0)x(1, 1, 2, 12) RMSE=1365.251\n",
            "SARIMA(0, 1, 0)x(1, 1, 3, 12) RMSE=1369.597\n",
            "SARIMA(0, 1, 0)x(2, 0, 0, 12) RMSE=1448.881\n",
            "SARIMA(0, 1, 0)x(2, 0, 1, 12) RMSE=1433.349\n",
            "SARIMA(0, 1, 0)x(2, 0, 2, 12) RMSE=1442.789\n",
            "SARIMA(0, 1, 0)x(2, 0, 3, 12) RMSE=1421.075\n",
            "SARIMA(0, 1, 0)x(2, 1, 0, 12) RMSE=1363.886\n",
            "SARIMA(0, 1, 0)x(2, 1, 1, 12) RMSE=1381.725\n",
            "SARIMA(0, 1, 0)x(2, 1, 2, 12) RMSE=1387.130\n",
            "SARIMA(0, 1, 0)x(2, 1, 3, 12) RMSE=1439.154\n",
            "SARIMA(0, 1, 0)x(3, 0, 0, 12) RMSE=1407.291\n",
            "SARIMA(0, 1, 0)x(3, 0, 1, 12) RMSE=1412.855\n",
            "SARIMA(0, 1, 0)x(3, 0, 2, 12) RMSE=1418.127\n",
            "SARIMA(0, 1, 0)x(3, 0, 3, 12) RMSE=1430.459\n",
            "SARIMA(0, 1, 0)x(3, 1, 0, 12) RMSE=1437.362\n",
            "SARIMA(0, 1, 0)x(3, 1, 1, 12) RMSE=1380.112\n",
            "SARIMA(0, 1, 0)x(3, 1, 2, 12) RMSE=1382.719\n",
            "SARIMA(0, 1, 0)x(3, 1, 3, 12) RMSE=1388.938\n",
            "SARIMA(0, 1, 0)x(4, 0, 0, 12) RMSE=1423.655\n",
            "SARIMA(0, 1, 0)x(4, 0, 1, 12) RMSE=1432.308\n",
            "SARIMA(0, 1, 0)x(4, 0, 2, 12) RMSE=1418.916\n",
            "SARIMA(0, 1, 0)x(4, 0, 3, 12) RMSE=1432.885\n",
            "SARIMA(0, 1, 0)x(4, 1, 0, 12) RMSE=1414.303\n",
            "SARIMA(0, 1, 0)x(4, 1, 1, 12) RMSE=1424.795\n",
            "SARIMA(0, 1, 0)x(4, 1, 2, 12) RMSE=1427.224\n",
            "SARIMA(0, 1, 1)x(0, 0, 0, 12) RMSE=1112.145\n",
            "SARIMA(0, 1, 1)x(0, 0, 1, 12) RMSE=1114.540\n",
            "SARIMA(0, 1, 1)x(0, 0, 2, 12) RMSE=1118.074\n",
            "SARIMA(0, 1, 1)x(0, 0, 3, 12) RMSE=1130.758\n",
            "SARIMA(0, 1, 1)x(0, 1, 0, 12) RMSE=1196.870\n",
            "SARIMA(0, 1, 1)x(0, 1, 1, 12) RMSE=1137.437\n",
            "SARIMA(0, 1, 1)x(0, 1, 2, 12) RMSE=1155.539\n",
            "SARIMA(0, 1, 1)x(0, 1, 3, 12) RMSE=1168.255\n",
            "SARIMA(0, 1, 1)x(1, 0, 0, 12) RMSE=1114.346\n",
            "SARIMA(0, 1, 1)x(1, 0, 1, 12) RMSE=1110.219\n",
            "SARIMA(0, 1, 1)x(1, 0, 2, 12) RMSE=1133.847\n",
            "SARIMA(0, 1, 1)x(1, 0, 3, 12) RMSE=1121.818\n",
            "SARIMA(0, 1, 1)x(1, 1, 0, 12) RMSE=1162.785\n",
            "SARIMA(0, 1, 1)x(1, 1, 1, 12) RMSE=1157.778\n",
            "SARIMA(0, 1, 1)x(1, 1, 2, 12) RMSE=1159.368\n",
            "SARIMA(0, 1, 1)x(1, 1, 3, 12) RMSE=1155.625\n",
            "SARIMA(0, 1, 1)x(2, 0, 0, 12) RMSE=1124.977\n",
            "SARIMA(0, 1, 1)x(2, 0, 1, 12) RMSE=1121.213\n",
            "SARIMA(0, 1, 1)x(2, 0, 2, 12) RMSE=1162.669\n",
            "SARIMA(0, 1, 1)x(2, 0, 3, 12) RMSE=1107.492\n",
            "SARIMA(0, 1, 1)x(2, 1, 0, 12) RMSE=1161.475\n",
            "SARIMA(0, 1, 1)x(2, 1, 1, 12) RMSE=1173.110\n",
            "SARIMA(0, 1, 1)x(2, 1, 2, 12) RMSE=1192.209\n",
            "SARIMA(0, 1, 1)x(2, 1, 3, 12) RMSE=1155.648\n",
            "SARIMA(0, 1, 1)x(3, 0, 0, 12) RMSE=1138.828\n",
            "SARIMA(0, 1, 1)x(3, 0, 1, 12) RMSE=1113.760\n",
            "SARIMA(0, 1, 1)x(3, 0, 2, 12) RMSE=1127.101\n",
            "SARIMA(0, 1, 1)x(3, 0, 3, 12) RMSE=1116.446\n",
            "SARIMA(0, 1, 1)x(3, 1, 0, 12) RMSE=1172.881\n",
            "SARIMA(0, 1, 1)x(3, 1, 1, 12) RMSE=1168.017\n",
            "SARIMA(0, 1, 1)x(3, 1, 2, 12) RMSE=1168.575\n",
            "SARIMA(0, 1, 1)x(3, 1, 3, 12) RMSE=1158.325\n",
            "SARIMA(0, 1, 1)x(4, 0, 0, 12) RMSE=1134.360\n",
            "SARIMA(0, 1, 1)x(4, 0, 1, 12) RMSE=1107.847\n",
            "SARIMA(0, 1, 1)x(4, 0, 2, 12) RMSE=1143.189\n",
            "SARIMA(0, 1, 1)x(4, 0, 3, 12) RMSE=1121.175\n",
            "SARIMA(0, 1, 1)x(4, 1, 0, 12) RMSE=1166.454\n",
            "SARIMA(0, 1, 1)x(4, 1, 1, 12) RMSE=1210.823\n",
            "SARIMA(0, 1, 1)x(4, 1, 3, 12) RMSE=1150.312\n",
            "SARIMA(0, 1, 2)x(0, 0, 0, 12) RMSE=1102.988\n",
            "SARIMA(0, 1, 2)x(0, 0, 1, 12) RMSE=1114.379\n",
            "SARIMA(0, 1, 2)x(0, 0, 2, 12) RMSE=1125.156\n",
            "SARIMA(0, 1, 2)x(0, 0, 3, 12) RMSE=1153.566\n",
            "SARIMA(0, 1, 2)x(0, 1, 0, 12) RMSE=1236.796\n",
            "SARIMA(0, 1, 2)x(0, 1, 1, 12) RMSE=1178.459\n",
            "SARIMA(0, 1, 2)x(0, 1, 2, 12) RMSE=1167.771\n",
            "SARIMA(0, 1, 2)x(0, 1, 3, 12) RMSE=1382.169\n",
            "SARIMA(0, 1, 2)x(1, 0, 0, 12) RMSE=1114.204\n",
            "SARIMA(0, 1, 2)x(1, 0, 1, 12) RMSE=1111.156\n",
            "SARIMA(0, 1, 2)x(1, 0, 2, 12) RMSE=1138.942\n",
            "SARIMA(0, 1, 2)x(1, 0, 3, 12) RMSE=1138.217\n",
            "SARIMA(0, 1, 2)x(1, 1, 0, 12) RMSE=1147.233\n",
            "SARIMA(0, 1, 2)x(1, 1, 1, 12) RMSE=1175.714\n",
            "SARIMA(0, 1, 2)x(1, 1, 2, 12) RMSE=1136.436\n",
            "SARIMA(0, 1, 2)x(1, 1, 3, 12) RMSE=1127.582\n",
            "SARIMA(0, 1, 2)x(2, 0, 0, 12) RMSE=1175.980\n",
            "SARIMA(0, 1, 2)x(2, 0, 1, 12) RMSE=1177.925\n",
            "SARIMA(0, 1, 2)x(2, 0, 2, 12) RMSE=1119.102\n",
            "SARIMA(0, 1, 2)x(2, 0, 3, 12) RMSE=1162.029\n",
            "SARIMA(0, 1, 2)x(2, 1, 0, 12) RMSE=1145.998\n",
            "SARIMA(0, 1, 2)x(2, 1, 1, 12) RMSE=1138.154\n",
            "SARIMA(0, 1, 2)x(2, 1, 2, 12) RMSE=1386.212\n",
            "SARIMA(0, 1, 2)x(2, 1, 3, 12) RMSE=1177.452\n",
            "SARIMA(0, 1, 2)x(3, 0, 0, 12) RMSE=1184.464\n",
            "SARIMA(0, 1, 2)x(3, 0, 1, 12) RMSE=1175.074\n",
            "SARIMA(0, 1, 2)x(3, 0, 2, 12) RMSE=1159.136\n",
            "SARIMA(0, 1, 2)x(3, 0, 3, 12) RMSE=1157.153\n",
            "SARIMA(0, 1, 2)x(3, 1, 0, 12) RMSE=1192.922\n",
            "SARIMA(0, 1, 2)x(3, 1, 1, 12) RMSE=1147.185\n",
            "SARIMA(0, 1, 2)x(3, 1, 2, 12) RMSE=1146.895\n",
            "SARIMA(0, 1, 2)x(3, 1, 3, 12) RMSE=1200.871\n",
            "SARIMA(0, 1, 2)x(4, 0, 0, 12) RMSE=1188.753\n",
            "SARIMA(0, 1, 2)x(4, 0, 1, 12) RMSE=1177.442\n",
            "SARIMA(0, 1, 2)x(4, 0, 2, 12) RMSE=1187.784\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHU6KELnMh9k",
        "outputId": "7ef08154-bc92-4cd2-9d42-a97afc2c9d63"
      },
      "source": [
        "ndiffs(df1, alpha=0.05, test='adf', max_d=6)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VlEC4EawMh9u",
        "outputId": "f417d509-caa3-4b75-b2ec-721e944a5cc4"
      },
      "source": [
        "adf_test(df2.dropna())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Results of Dickey-Fuller Test:\n",
            "Test Statistic                -6.621722e+00\n",
            "p-value                        6.013716e-09\n",
            "#Lags Used                     4.000000e+00\n",
            "Number of Observations Used    8.600000e+01\n",
            "Critical Value (1%)           -3.508783e+00\n",
            "Critical Value (5%)           -2.895784e+00\n",
            "Critical Value (10%)          -2.585038e+00\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9jmbfcsMh95"
      },
      "source": [
        "cf.set_config_file(offline_show_link=False)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}