BaselineModels_Forecasting_ArcticSeaIce.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1PYydqHjUMan",
        "outputId": "6be9f901-c2aa-4099-fc64-3abed40690b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scalecast\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from scalecast.Forecaster import Forecaster\n",
        "from scalecast.auxmodels import auto_arima\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "!pip install sktime\n",
        "!pip install pmdarima\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eQqGnbmVT0dL",
        "outputId": "037947b9-a6f6-460d-9c47-ef1a6ad49a0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# season = 'SpringSummer'\n",
        "# ind = ''\n",
        "# mode = 'VolTime_'\n",
        "# Vol = pd.read_csv('/content/gdrive/MyDrive/ARCTICseaForecasting/PIOMAS/ICEF7d_Volsequences_' + season + '.csv',header=None)\n",
        "# Vol.iloc[:,:] = Vol.iloc[:,:]*1000\n",
        "# TS =  pd.read_csv('/content/gdrive/MyDrive/ARCTICseaForecasting/PIOMAS/ICEF7d_TSsequences_' + season + '.csv',header=None)\n",
        "# cols = TS.columns[:]\n",
        "# TS[cols] = TS[cols].apply(pd.to_datetime, errors='coerce')"
      ],
      "metadata": {
        "id": "LEG1Pr2Uyc3Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Vol = pd.read_csv('/content/gdrive/MyDrive/ARCTICseaForecasting/PIOMAS/IceTimeSeries.csv')\n",
        "Vol['Volume'] = Vol['Volume']*1000 \n",
        "Vol['Time'] = Vol['Time'].apply(pd.to_datetime, errors='coerce')\n",
        "TS = Vol['Time']\n",
        "Vol.set_index('Time', inplace=True)\n",
        "Vol = Vol.drop(columns=['Thickness'])\n",
        "Vol = Vol.asfreq('D')\n",
        "Vol = Vol.fillna(0)\n",
        "Vol# Vol = Vol['Volume']\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "jAyC3DzsUiDb",
        "outputId": "7671839e-d496-460c-c103-21b07600ed83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Volume\n",
              "Time               \n",
              "1979-01-01  26405.0\n",
              "1979-01-02  26496.0\n",
              "1979-01-03  26582.0\n",
              "1979-01-04  26672.0\n",
              "1979-01-05  26770.0\n",
              "...             ...\n",
              "2022-06-26  14660.0\n",
              "2022-06-27  14330.0\n",
              "2022-06-28  13975.0\n",
              "2022-06-29  13719.0\n",
              "2022-06-30  13479.0\n",
              "\n",
              "[15887 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e7f1dc96-2258-4b12-a86c-7d5790bb9991\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1979-01-01</th>\n",
              "      <td>26405.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1979-01-02</th>\n",
              "      <td>26496.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1979-01-03</th>\n",
              "      <td>26582.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1979-01-04</th>\n",
              "      <td>26672.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1979-01-05</th>\n",
              "      <td>26770.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-26</th>\n",
              "      <td>14660.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-27</th>\n",
              "      <td>14330.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-28</th>\n",
              "      <td>13975.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-29</th>\n",
              "      <td>13719.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-30</th>\n",
              "      <td>13479.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>15887 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7f1dc96-2258-4b12-a86c-7d5790bb9991')\"\n",
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              "          const element = document.querySelector('#df-e7f1dc96-2258-4b12-a86c-7d5790bb9991');\n",
              "          const dataTable =\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# X_train, X_test = Vol.iloc[:TS.shape[0]-int(TS.shape[0]*20/100),:30], Vol.iloc[-int(TS.shape[0]*20/100):,:30]\n",
        "# y_train, y_test = Vol.iloc[:TS.shape[0]-int(TS.shape[0]*20/100),:30], Vol.iloc[-int(TS.shape[0]*20/100):,:30]\n",
        "y_train = Vol.iloc[:TS.shape[0]-int(TS.shape[0]*20/100),:]\n",
        "y_test = Vol.iloc[-int(TS.shape[0]*20/100):,:]"
      ],
      "metadata": {
        "id": "ZjHO3fF5hcgf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "int(TS.shape[0]*20/100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixWJQLQPbOZn",
        "outputId": "d1d14332-0ca2-45e6-ee77-007333be1935"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3175"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # step 2: specifying forecasting horizon\n",
        "# from sktime.forecasting.base import ForecastingHorizon\n",
        "# fh = np.arange(1, 8)\n",
        "\n",
        "# # step 3: specifying the forecasting algorithm\n",
        "# from sktime.forecasting.naive import NaiveForecaster\n",
        "# # forecaster = NaiveForecaster(strategy=\"last\")\n",
        "# # from sktime.forecasting.arima import AutoARIMA\n",
        "# forecaster = AutoARIMA(suppress_warnings=True)\n",
        "\n",
        "# # step 4: fitting the forecaster\n",
        "# forecaster.fit(y_train, fh=fh)\n",
        "\n",
        "# # step 5: querying predictions\n",
        "# y_pred = forecaster.predict(fh)\n",
        "# y_pred"
      ],
      "metadata": {
        "id": "It9CX17phH5V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sktime.forecasting.base import ForecastingHorizon\n",
        "from sktime.forecasting.compose import make_reduction\n",
        "from sklearn.svm import SVR\n",
        "\n",
        "fh = ForecastingHorizon(y_test.index, is_relative=False)\n",
        "\n",
        "regressor = SVR(kernel='sigmoid')\n",
        "mdl = 'SVRsigmoid'\n",
        "forecaster = make_reduction(regressor, window_length=30, strategy=\"recursive\")\n",
        "forecaster.fit(y_train)\n",
        "y_pred = forecaster.predict(fh)\n",
        "y_pred"
      ],
      "metadata": {
        "id": "E0g6gqGIu8ZQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d = pd.DataFrame([])\n",
        "d = y_test\n",
        "np.savetxt('/content/gdrive/MyDrive/ARCTICseaForecasting/PIOMAS/export/BaselineModels_test_targetdata_VOLF.csv', d, delimiter=',')\n",
        "\n",
        "dp = pd.DataFrame([])\n",
        "dp = y_pred\n",
        "np.savetxt('/content/gdrive/MyDrive/ARCTICseaForecasting/PIOMAS/export/BaselineModels_test_predsdata_VOLF_' + mdl +'.csv', dp, delimiter=',')\n"
      ],
      "metadata": {
        "id": "HmHJl3xH6zYk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sktime.forecasting.base import ForecastingHorizon\n",
        "from sktime.forecasting.compose import make_reduction\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "\n",
        "fh = ForecastingHorizon(y_test.index, is_relative=False)\n",
        "\n",
        "regressor = GradientBoostingRegressor()\n",
        "mdl = 'GBDT'\n",
        "forecaster = make_reduction(regressor, window_length=30, strategy=\"recursive\")\n",
        "forecaster.fit(y_train)\n",
        "y_pred = forecaster.predict(fh)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "FCZ1_Pl7Ek79",
        "outputId": "f1ce0579-4dab-486e-fb10-660b670afd5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Volume\n",
              "2013-10-21  6275.876824\n",
              "2013-10-22  6275.876824\n",
              "2013-10-23  6275.876824\n",
              "2013-10-24  6275.876824\n",
              "2013-10-25  6275.876824\n",
              "...                 ...\n",
              "2022-06-26  6275.876824\n",
              "2022-06-27  6275.876824\n",
              "2022-06-28  6275.876824\n",
              "2022-06-29  6275.876824\n",
              "2022-06-30  6275.876824\n",
              "\n",
              "[3175 rows x 1 columns]"
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              "      <th>Volume</th>\n",
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              "    <tr>\n",
              "      <th>2013-10-21</th>\n",
              "      <td>6275.876824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-22</th>\n",
              "      <td>6275.876824</td>\n",
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              "    <tr>\n",
              "      <th>2013-10-23</th>\n",
              "      <td>6275.876824</td>\n",
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              "    <tr>\n",
              "      <th>2013-10-24</th>\n",
              "      <td>6275.876824</td>\n",
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              "    <tr>\n",
              "      <th>2013-10-25</th>\n",
              "      <td>6275.876824</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>6275.876824</td>\n",
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              "    <tr>\n",
              "      <th>2022-06-27</th>\n",
              "      <td>6275.876824</td>\n",
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              "    <tr>\n",
              "      <th>2022-06-28</th>\n",
              "      <td>6275.876824</td>\n",
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              "<p>3175 rows × 1 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ca5431b-f12d-477f-a9ed-fac3b60cb6ec')\"\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2ca5431b-f12d-477f-a9ed-fac3b60cb6ec');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d = pd.DataFrame([])\n",
        "d = y_test\n",
        "np.savetxt('/content/gdrive/MyDrive/ARCTICseaForecasting/PIOMAS/export/BaselineModels_test_targetdata_VOLF.csv', d, delimiter=',')\n",
        "\n",
        "dp = pd.DataFrame([])\n",
        "dp = y_pred\n",
        "np.savetxt('/content/gdrive/MyDrive/ARCTICseaForecasting/PIOMAS/export/BaselineModels_test_predsdata_VOLF_' + mdl +'.csv', dp, delimiter=',')"
      ],
      "metadata": {
        "id": "QDmkxaNMEk-P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sktime.forecasting.base import ForecastingHorizon\n",
        "from sktime.forecasting.compose import make_reduction\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "fh = ForecastingHorizon(y_test.index, is_relative=False)\n",
        "\n",
        "regressor = RandomForestRegressor()\n",
        "mdl = 'RF'\n",
        "forecaster = make_reduction(regressor, window_length=30, strategy=\"recursive\")\n",
        "forecaster.fit(y_train)\n",
        "y_pred = forecaster.predict(fh)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "TmasfCy0JZTk",
        "outputId": "ded1d9c6-2b42-46f7-d9bf-b19fb72c210a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Volume\n",
              "2013-10-21   7216.98\n",
              "2013-10-22   7333.04\n",
              "2013-10-23   7436.61\n",
              "2013-10-24   7531.10\n",
              "2013-10-25   7598.94\n",
              "...              ...\n",
              "2022-06-26  24667.90\n",
              "2022-06-27  24656.74\n",
              "2022-06-28  24667.11\n",
              "2022-06-29  24661.28\n",
              "2022-06-30  24667.90\n",
              "\n",
              "[3175 rows x 1 columns]"
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              "      <th>2013-10-21</th>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-26</th>\n",
              "      <td>24667.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-27</th>\n",
              "      <td>24656.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-28</th>\n",
              "      <td>24667.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-29</th>\n",
              "      <td>24661.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-30</th>\n",
              "      <td>24667.90</td>\n",
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "d = pd.DataFrame([])\n",
        "d = y_test\n",
        "np.savetxt('/content/gdrive/MyDrive/ARCTICseaForecasting/PIOMAS/export/BaselineModels_test_targetdata_VOLF.csv', d, delimiter=',')\n",
        "\n",
        "dp = pd.DataFrame([])\n",
        "dp = y_pred\n",
        "np.savetxt('/content/gdrive/MyDrive/ARCTICseaForecasting/PIOMAS/export/BaselineModels_test_predsdata_VOLF_' + mdl +'.csv', dp, delimiter=',')"
      ],
      "metadata": {
        "id": "LGFG0g2gJmNn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sktime.forecasting.arima import AutoARIMA\n",
        "from sktime.forecasting.base import ForecastingHorizon\n",
        "from sktime.forecasting.compose import make_reduction\n",
        "\n",
        "fh = ForecastingHorizon(y_test.index, is_relative=False)\n",
        "\n",
        "mdl = 'ARIMA'\n",
        "forecaster = AutoARIMA(sp=12, suppress_warnings=True) \n",
        "forecaster.fit(y_train)\n",
        "y_pred = forecaster.predict(fh)\n",
        "y_pred"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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        "id": "xN4DeIEDElAk",
        "outputId": "a590d9b5-a176-4ee0-e553-264be0b92eb7"
      },
      "execution_count": null,
      "outputs": [
        {
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              "                  Volume\n",
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              "2022-06-29  11511.175294\n",
              "2022-06-30  11511.175294\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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    {
      "cell_type": "code",
      "source": [
        "forecaster.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "cVo7dYEaIJ5p",
        "outputId": "1b6c11bb-ad6a-49a2-f2ec-b26b19f0e50e"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                               SARIMAX Results                                \n",
              "==============================================================================\n",
              "Dep. Variable:                      y   No. Observations:                12701\n",
              "Model:               SARIMAX(1, 1, 5)   Log Likelihood              -99615.825\n",
              "Date:                Thu, 25 Aug 2022   AIC                         199245.650\n",
              "Time:                        13:49:46   BIC                         199297.796\n",
              "Sample:                    01-01-1979   HQIC                        199263.093\n",
              "                         - 10-09-2013                                         \n",
              "Covariance Type:                  opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "ar.L1          0.9915      0.002    604.681      0.000       0.988       0.995\n",
              "ma.L1         -1.7930      0.002   -908.810      0.000      -1.797      -1.789\n",
              "ma.L2          0.8052      0.003    268.063      0.000       0.799       0.811\n",
              "ma.L3          0.0054      0.003      1.622      0.105      -0.001       0.012\n",
              "ma.L4         -0.0182      0.004     -4.898      0.000      -0.025      -0.011\n",
              "ma.L5          0.0245      0.003      9.745      0.000       0.020       0.029\n",
              "sigma2      3.775e+05    312.061   1209.785      0.000    3.77e+05    3.78e+05\n",
              "===================================================================================\n",
              "Ljung-Box (L1) (Q):                   0.04   Jarque-Bera (JB):         568735246.38\n",
              "Prob(Q):                              0.84   Prob(JB):                         0.00\n",
              "Heteroskedasticity (H):               0.52   Skew:                           -28.26\n",
              "Prob(H) (two-sided):                  0.00   Kurtosis:                      1038.17\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>SARIMAX Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>12701</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>SARIMAX(1, 1, 5)</td> <th>  Log Likelihood     </th> <td>-99615.825</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>            <td>Thu, 25 Aug 2022</td> <th>  AIC                </th> <td>199245.650</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                <td>13:49:46</td>     <th>  BIC                </th> <td>199297.796</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>             <td>01-01-1979</td>    <th>  HQIC               </th> <td>199263.093</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                   <td>- 10-09-2013</td>   <th>                     </th>      <td> </td>    \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>      <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L1</th>  <td>    0.9915</td> <td>    0.002</td> <td>  604.681</td> <td> 0.000</td> <td>    0.988</td> <td>    0.995</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1</th>  <td>   -1.7930</td> <td>    0.002</td> <td> -908.810</td> <td> 0.000</td> <td>   -1.797</td> <td>   -1.789</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L2</th>  <td>    0.8052</td> <td>    0.003</td> <td>  268.063</td> <td> 0.000</td> <td>    0.799</td> <td>    0.811</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L3</th>  <td>    0.0054</td> <td>    0.003</td> <td>    1.622</td> <td> 0.105</td> <td>   -0.001</td> <td>    0.012</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L4</th>  <td>   -0.0182</td> <td>    0.004</td> <td>   -4.898</td> <td> 0.000</td> <td>   -0.025</td> <td>   -0.011</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L5</th>  <td>    0.0245</td> <td>    0.003</td> <td>    9.745</td> <td> 0.000</td> <td>    0.020</td> <td>    0.029</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th> <td> 3.775e+05</td> <td>  312.061</td> <td> 1209.785</td> <td> 0.000</td> <td> 3.77e+05</td> <td> 3.78e+05</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (L1) (Q):</th>     <td>0.04</td> <th>  Jarque-Bera (JB):  </th> <td>568735246.38</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.84</td> <th>  Prob(JB):          </th>     <td>0.00</td>    \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>0.52</td> <th>  Skew:              </th>    <td>-28.26</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>    <td>1038.17</td>  \n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mdl = 'SARIMAX'\n",
        "d = pd.DataFrame([])\n",
        "d = y_test\n",
        "np.savetxt('/content/gdrive/MyDrive/ARCTICseaForecasting/PIOMAS/export/BaselineModels_test_targetdata_VOLF.csv', d, delimiter=',')\n",
        "\n",
        "dp = pd.DataFrame([])\n",
        "dp = y_pred\n",
        "np.savetxt('/content/gdrive/MyDrive/ARCTICseaForecasting/PIOMAS/export/BaselineModels_test_predsdata_VOLF_' + mdl +'.csv', dp, delimiter=',')"
      ],
      "metadata": {
        "id": "bkubuJ8aIJ3V"
      },
      "execution_count": null,
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    }
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