nbeats.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "055-TAWm6sS5"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "tduxHKLl7GPY",
        "outputId": "214571a7-5537-4591-a6f4-eb17e875cce2"
      },
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>IPG2211A2N</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01-21</td>\n",
              "      <td>431000</td>\n",
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              "      <th>1</th>\n",
              "      <td>2018-01-28</td>\n",
              "      <td>379000</td>\n",
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              "      <th>2</th>\n",
              "      <td>2018-02-04</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-02-11</td>\n",
              "      <td>577000</td>\n",
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              "      <th>4</th>\n",
              "      <td>2018-02-18</td>\n",
              "      <td>0</td>\n",
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              "      <th>215</th>\n",
              "      <td>2022-03-06</td>\n",
              "      <td>1282000</td>\n",
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              "      <th>216</th>\n",
              "      <td>2022-03-13</td>\n",
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              "      <th>217</th>\n",
              "      <td>2022-03-20</td>\n",
              "      <td>1339000</td>\n",
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              "      <td>1385000</td>\n",
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              "    <tr>\n",
              "      <th>219</th>\n",
              "      <td>2022-04-03</td>\n",
              "      <td>584000</td>\n",
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              "        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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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "           DATE  IPG2211A2N\n",
              "0    2018-01-21      431000\n",
              "1    2018-01-28      379000\n",
              "2    2018-02-04           0\n",
              "3    2018-02-11      577000\n",
              "4    2018-02-18           0\n",
              "..          ...         ...\n",
              "215  2022-03-06     1282000\n",
              "216  2022-03-13     1289000\n",
              "217  2022-03-20     1339000\n",
              "218  2022-03-27     1385000\n",
              "219  2022-04-03      584000\n",
              "\n",
              "[220 rows x 2 columns]"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = pd.read_csv(\"/content/1369154005_200005_sellOut.csv\")\n",
        "data = data.drop(['Unnamed: 0'], axis = 1)\n",
        "data.columns = ['DATE', 'IPG2211A2N']\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "KcC3Jb3WEllW",
        "outputId": "b6abd63e-2de7-4f92-8441-532516e7c396"
      },
      "outputs": [
        {
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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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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b6ac5495-5e5a-469f-9737-63bbbaaf53be button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         index  IPG2211A2N\n",
              "0   2018-01-21      431000\n",
              "1   2018-01-28      379000\n",
              "2   2018-02-04           0\n",
              "3   2018-02-11      577000\n",
              "4   2018-02-18           0\n",
              "..         ...         ...\n",
              "215 2022-03-06     1282000\n",
              "216 2022-03-13     1289000\n",
              "217 2022-03-20     1339000\n",
              "218 2022-03-27     1385000\n",
              "219 2022-04-03      584000\n",
              "\n",
              "[220 rows x 2 columns]"
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['DATE'] = pd.to_datetime(data['DATE'])\n",
        "data.sort_values(by='DATE', inplace=True)\n",
        "data.columns = ['index', 'IPG2211A2N']\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "L2IcMfqp_51b",
        "outputId": "9895b358-c773-461a-9d94-c2dc85be2749"
      },
      "outputs": [],
      "source": [
        "data['DATE'] = pd.to_datetime(data['DATE'])\n",
        "time_diff = data['DATE'].diff()\n",
        "expected_interval = pd.Timedelta(hours=1)\n",
        "gaps = time_diff[time_diff > expected_interval]\n",
        "if not gaps.empty:\n",
        "    print(\"Gaps detected in the time series data.\")\n",
        "    print(gaps)\n",
        "else:\n",
        "    print(\"No gaps detected in the time series data.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "ArJw16GzGyFd",
        "outputId": "769667ef-06ae-490b-fbdc-fa5f45aaf200"
      },
      "outputs": [],
      "source": [
        "# Set 'Datetime' as the index\n",
        "data.set_index('DATE', inplace=True)\n",
        "\n",
        "# Generate a date range from the first date to the last date with hourly frequency\n",
        "date_range = pd.date_range(start=data.index.min(), end=data.index.max(), freq='D')\n",
        "\n",
        "# Reindex the DataFrame with the complete date range\n",
        "df = data.reindex(date_range)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PmHR92uVh_9K"
      },
      "outputs": [],
      "source": [
        "# Use forward fill (LOCF) to fill missing values\n",
        "df['IPG2211A2N'].fillna(method='ffill', inplace=True)\n",
        "\n",
        "# Reset the index to have 'Datetime' as a regular column\n",
        "df.reset_index(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cWfuJW8cBzZJ"
      },
      "outputs": [],
      "source": [
        "df = df.assign(FillMean=df.IPG2211A2N.fillna(df.IPG2211A2N.mean()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "DD0i5kcpCDem",
        "outputId": "13d41a64-e2dc-418a-90e0-0cde579661ac"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6214124b-812f-4526-bdc9-6dd8f3f9aa75\" class=\"colab-df-container\">\n",
              "    <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>index</th>\n",
              "      <th>IPG2211A2N</th>\n",
              "      <th>FillMean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1985-01-01</td>\n",
              "      <td>72.5052</td>\n",
              "      <td>72.5052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1985-01-02</td>\n",
              "      <td>72.5052</td>\n",
              "      <td>72.5052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1985-01-03</td>\n",
              "      <td>72.5052</td>\n",
              "      <td>72.5052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1985-01-04</td>\n",
              "      <td>72.5052</td>\n",
              "      <td>72.5052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1985-01-05</td>\n",
              "      <td>72.5052</td>\n",
              "      <td>72.5052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>12049</th>\n",
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              "      <td>114.7212</td>\n",
              "      <td>114.7212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12050</th>\n",
              "      <td>2017-12-29</td>\n",
              "      <td>114.7212</td>\n",
              "      <td>114.7212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12051</th>\n",
              "      <td>2017-12-30</td>\n",
              "      <td>114.7212</td>\n",
              "      <td>114.7212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12052</th>\n",
              "      <td>2017-12-31</td>\n",
              "      <td>114.7212</td>\n",
              "      <td>114.7212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12053</th>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>129.4048</td>\n",
              "      <td>129.4048</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12054 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6214124b-812f-4526-bdc9-6dd8f3f9aa75')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6214124b-812f-4526-bdc9-6dd8f3f9aa75 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6214124b-812f-4526-bdc9-6dd8f3f9aa75');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e6084bec-266a-462c-9164-fdbb8249e0cf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e6084bec-266a-462c-9164-fdbb8249e0cf')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e6084bec-266a-462c-9164-fdbb8249e0cf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "           index  IPG2211A2N  FillMean\n",
              "0     1985-01-01     72.5052   72.5052\n",
              "1     1985-01-02     72.5052   72.5052\n",
              "2     1985-01-03     72.5052   72.5052\n",
              "3     1985-01-04     72.5052   72.5052\n",
              "4     1985-01-05     72.5052   72.5052\n",
              "...          ...         ...       ...\n",
              "12049 2017-12-28    114.7212  114.7212\n",
              "12050 2017-12-29    114.7212  114.7212\n",
              "12051 2017-12-30    114.7212  114.7212\n",
              "12052 2017-12-31    114.7212  114.7212\n",
              "12053 2018-01-01    129.4048  129.4048\n",
              "\n",
              "[12054 rows x 3 columns]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fCjeF21tMsKM"
      },
      "outputs": [],
      "source": [
        "df=data.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "zgB8FttciNxN",
        "outputId": "0455cc3e-b66d-44a2-b8c2-decf12b2571c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "plt.plot(df['index'], df['IPG2211A2N'], label='fulldata')\n",
        "# plt.plot(test_data['Datetime'],test_data['AEP_MW'], label='Test')\n",
        "# plt.plot(test_data['Datetime'],test_data['moving_avg_forecast'], label='Moving Average Forecast using 10 observations')\n",
        "plt.legend(loc='best')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('AEP_MW')\n",
        "plt.title('AEP_MW and Moving Average Forecast')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5kniNpxqAjN5"
      },
      "outputs": [],
      "source": [
        "# start_date = '2004-10-01 01:00:00'\n",
        "# end_date = '2004-12-01 01:00:00'\n",
        "\n",
        "# selected_data = data[(data['Datetime'] >= start_date) & (data['Datetime'] <= end_date)]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0bI3kdgBWRCQ"
      },
      "outputs": [],
      "source": [
        "df['time_idx'] = range(len(df))\n",
        "# df = df.drop(['FillMean'], axis=1)\n",
        "constant_value = 'series'\n",
        "df['series'] = 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7jPhq4hGDgVy"
      },
      "outputs": [],
      "source": [
        "# split_date = '2014-01-01'\n",
        "# split_index = len(df[df['index'] < split_date])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5JWzgJjtFSaK"
      },
      "outputs": [],
      "source": [
        "train_data = df.iloc[:200]\n",
        "test_data = df.iloc[200:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "rcqyTL27FZVi",
        "outputId": "15b1fced-e390-45da-8833-b6a06ef1f615"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>IPG2211A2N</th>\n",
              "      <th>time_idx</th>\n",
              "      <th>series</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01-21</td>\n",
              "      <td>431000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-01-28</td>\n",
              "      <td>379000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-02-04</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-02-11</td>\n",
              "      <td>577000</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-02-18</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>195</th>\n",
              "      <td>2021-10-17</td>\n",
              "      <td>1193000</td>\n",
              "      <td>195</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>2021-10-24</td>\n",
              "      <td>1018000</td>\n",
              "      <td>196</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>2021-10-31</td>\n",
              "      <td>821000</td>\n",
              "      <td>197</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>2021-11-07</td>\n",
              "      <td>1546000</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>2021-11-14</td>\n",
              "      <td>1129000</td>\n",
              "      <td>199</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "<p>200 rows × 4 columns</p>\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    60% {\n",
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              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e663cf50-93fc-4282-9b0a-c9194bcc44dc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         index  IPG2211A2N  time_idx  series\n",
              "0   2018-01-21      431000         0       1\n",
              "1   2018-01-28      379000         1       1\n",
              "2   2018-02-04           0         2       1\n",
              "3   2018-02-11      577000         3       1\n",
              "4   2018-02-18           0         4       1\n",
              "..         ...         ...       ...     ...\n",
              "195 2021-10-17     1193000       195       1\n",
              "196 2021-10-24     1018000       196       1\n",
              "197 2021-10-31      821000       197       1\n",
              "198 2021-11-07     1546000       198       1\n",
              "199 2021-11-14     1129000       199       1\n",
              "\n",
              "[200 rows x 4 columns]"
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.reset_index(drop=True, inplace=True)\n",
        "test_data.reset_index(drop=True, inplace=True)\n",
        "train_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "mMR7jLTNGj8X",
        "outputId": "a86852a7-5d74-4de0-c1d0-9b8468bd0b18"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>IPG2211A2N</th>\n",
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              "      <td>2021-11-28</td>\n",
              "      <td>1264000</td>\n",
              "      <td>201</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-12-05</td>\n",
              "      <td>1349000</td>\n",
              "      <td>202</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-12-12</td>\n",
              "      <td>1267000</td>\n",
              "      <td>203</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>930000</td>\n",
              "      <td>204</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2021-12-26</td>\n",
              "      <td>1243000</td>\n",
              "      <td>205</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2022-01-02</td>\n",
              "      <td>1384000</td>\n",
              "      <td>206</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2022-01-09</td>\n",
              "      <td>1045000</td>\n",
              "      <td>207</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2022-01-16</td>\n",
              "      <td>1307000</td>\n",
              "      <td>208</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2022-01-23</td>\n",
              "      <td>910000</td>\n",
              "      <td>209</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2022-01-30</td>\n",
              "      <td>1369000</td>\n",
              "      <td>210</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2022-02-06</td>\n",
              "      <td>974000</td>\n",
              "      <td>211</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2022-02-13</td>\n",
              "      <td>1354000</td>\n",
              "      <td>212</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2022-02-20</td>\n",
              "      <td>1270000</td>\n",
              "      <td>213</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2022-02-27</td>\n",
              "      <td>1254000</td>\n",
              "      <td>214</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2022-03-06</td>\n",
              "      <td>1282000</td>\n",
              "      <td>215</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2022-03-13</td>\n",
              "      <td>1289000</td>\n",
              "      <td>216</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2022-03-20</td>\n",
              "      <td>1339000</td>\n",
              "      <td>217</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2022-03-27</td>\n",
              "      <td>1385000</td>\n",
              "      <td>218</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2022-04-03</td>\n",
              "      <td>584000</td>\n",
              "      <td>219</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-86616a21-71ee-4bdf-b16e-c1f9ba7e1765')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-86616a21-71ee-4bdf-b16e-c1f9ba7e1765 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-86616a21-71ee-4bdf-b16e-c1f9ba7e1765');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6f9418ad-4fe2-42ee-a00f-5cc4496dc65e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6f9418ad-4fe2-42ee-a00f-5cc4496dc65e')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6f9418ad-4fe2-42ee-a00f-5cc4496dc65e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        index  IPG2211A2N  time_idx  series\n",
              "0  2021-11-21     1316000       200       1\n",
              "1  2021-11-28     1264000       201       1\n",
              "2  2021-12-05     1349000       202       1\n",
              "3  2021-12-12     1267000       203       1\n",
              "4  2021-12-19      930000       204       1\n",
              "5  2021-12-26     1243000       205       1\n",
              "6  2022-01-02     1384000       206       1\n",
              "7  2022-01-09     1045000       207       1\n",
              "8  2022-01-16     1307000       208       1\n",
              "9  2022-01-23      910000       209       1\n",
              "10 2022-01-30     1369000       210       1\n",
              "11 2022-02-06      974000       211       1\n",
              "12 2022-02-13     1354000       212       1\n",
              "13 2022-02-20     1270000       213       1\n",
              "14 2022-02-27     1254000       214       1\n",
              "15 2022-03-06     1282000       215       1\n",
              "16 2022-03-13     1289000       216       1\n",
              "17 2022-03-20     1339000       217       1\n",
              "18 2022-03-27     1385000       218       1\n",
              "19 2022-04-03      584000       219       1"
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zBlDCL6LcmTm",
        "outputId": "a9808aa8-008b-426c-e1af-7d734e893fe9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pytorch_forecasting in /usr/local/lib/python3.10/dist-packages (1.0.0)\n",
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            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>2021.06.0->lightning<3.0.0,>=2.0.0->pytorch_forecasting) (23.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>2021.06.0->lightning<3.0.0,>=2.0.0->pytorch_forecasting) (3.3.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>2021.06.0->lightning<3.0.0,>=2.0.0->pytorch_forecasting) (6.0.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>2021.06.0->lightning<3.0.0,>=2.0.0->pytorch_forecasting) (4.0.3)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>2021.06.0->lightning<3.0.0,>=2.0.0->pytorch_forecasting) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>2021.06.0->lightning<3.0.0,>=2.0.0->pytorch_forecasting) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<2025.0,>2021.06.0->lightning<3.0.0,>=2.0.0->pytorch_forecasting) (1.3.1)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->fsspec[http]<2025.0,>2021.06.0->lightning<3.0.0,>=2.0.0->pytorch_forecasting) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->fsspec[http]<2025.0,>2021.06.0->lightning<3.0.0,>=2.0.0->pytorch_forecasting) (2023.7.22)\n"
          ]
        }
      ],
      "source": [
        "!pip install pytorch_forecasting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "zXzqOOnjbaME",
        "outputId": "a75d6430-275a-4189-cb06-bd4849ac92da"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>IPG2211A2N</th>\n",
              "      <th>time_idx</th>\n",
              "      <th>series</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01-21</td>\n",
              "      <td>431000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-01-28</td>\n",
              "      <td>379000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-02-04</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-02-11</td>\n",
              "      <td>577000</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-02-18</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>215</th>\n",
              "      <td>2022-03-06</td>\n",
              "      <td>1282000</td>\n",
              "      <td>215</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>2022-03-13</td>\n",
              "      <td>1289000</td>\n",
              "      <td>216</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217</th>\n",
              "      <td>2022-03-20</td>\n",
              "      <td>1339000</td>\n",
              "      <td>217</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>218</th>\n",
              "      <td>2022-03-27</td>\n",
              "      <td>1385000</td>\n",
              "      <td>218</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>219</th>\n",
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              "      <td>219</td>\n",
              "      <td>1</td>\n",
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              "<p>220 rows × 4 columns</p>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
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              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-88a359d1-6521-41fa-baa7-bf51e1377627 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         index  IPG2211A2N  time_idx  series\n",
              "0   2018-01-21      431000         0       1\n",
              "1   2018-01-28      379000         1       1\n",
              "2   2018-02-04           0         2       1\n",
              "3   2018-02-11      577000         3       1\n",
              "4   2018-02-18           0         4       1\n",
              "..         ...         ...       ...     ...\n",
              "215 2022-03-06     1282000       215       1\n",
              "216 2022-03-13     1289000       216       1\n",
              "217 2022-03-20     1339000       217       1\n",
              "218 2022-03-27     1385000       218       1\n",
              "219 2022-04-03      584000       219       1\n",
              "\n",
              "[220 rows x 4 columns]"
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "yrL0hxEFNSIZ",
        "outputId": "3d044b92-b2ae-41dc-888d-cffa8352a6ad"
      },
      "outputs": [
        {
          "data": {
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              "      <th>3</th>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>2021-10-17</td>\n",
              "      <td>1193000</td>\n",
              "      <td>195</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>2021-10-24</td>\n",
              "      <td>1018000</td>\n",
              "      <td>196</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>2021-10-31</td>\n",
              "      <td>821000</td>\n",
              "      <td>197</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>2021-11-07</td>\n",
              "      <td>1546000</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>2021-11-14</td>\n",
              "      <td>1129000</td>\n",
              "      <td>199</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 4 columns</p>\n",
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              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-228fd548-b47f-4004-a999-c4f624d1dfa7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-228fd548-b47f-4004-a999-c4f624d1dfa7');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-38db2780-ac54-443c-b620-a30c7228fb6f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-38db2780-ac54-443c-b620-a30c7228fb6f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-38db2780-ac54-443c-b620-a30c7228fb6f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         index  IPG2211A2N  time_idx  series\n",
              "0   2018-01-21      431000         0       1\n",
              "1   2018-01-28      379000         1       1\n",
              "2   2018-02-04           0         2       1\n",
              "3   2018-02-11      577000         3       1\n",
              "4   2018-02-18           0         4       1\n",
              "..         ...         ...       ...     ...\n",
              "195 2021-10-17     1193000       195       1\n",
              "196 2021-10-24     1018000       196       1\n",
              "197 2021-10-31      821000       197       1\n",
              "198 2021-11-07     1546000       198       1\n",
              "199 2021-11-14     1129000       199       1\n",
              "\n",
              "[200 rows x 4 columns]"
            ]
          },
          "execution_count": 87,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6wRMkaN_PO3Y",
        "outputId": "edbb7da9-87fc-4c6d-c213-c16a537a2be1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "49"
            ]
          },
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df[df['IPG2211A2N']==0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nQq6DKwFYpja"
      },
      "outputs": [],
      "source": [
        "# max_prediction_length = 365 # a whole year\n",
        "# min_encoder_length = 365\n",
        "# max_encoder_length = train_data.index.nunique()\n",
        "max_encoder_length = 60\n",
        "max_prediction_length = 20\n",
        "\n",
        "\n",
        "context_length = max_encoder_length\n",
        "prediction_length = max_prediction_length\n",
        "\n",
        "# keep the validation set held-out\n",
        "training_cutoff = train_data[\"time_idx\"].max() - max_prediction_length"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N-HXOix4OPRJ"
      },
      "outputs": [],
      "source": [
        "# from pytorch_forecasting.data.encoders import MultiCategoryEncoder\n",
        "# encoder = MultiCategoryEncoder(add_nan=True)\n",
        "# transformed_train = encoder.transform(train_data)\n",
        "# transformed_test = encoder.transform(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g__IA6XTZSet"
      },
      "outputs": [],
      "source": [
        "\n",
        "from pytorch_forecasting import NBeats, TimeSeriesDataSet\n",
        "from pytorch_forecasting import NaNLabelEncoder\n",
        "\n",
        "# Create training set\n",
        "training_dataset = TimeSeriesDataSet(\n",
        "    train_data[lambda x: x.time_idx <= training_cutoff],\n",
        "    time_idx=\"time_idx\",\n",
        "    target=\"IPG2211A2N\", # target variable\n",
        "    group_ids=[\"series\"], # static covariates\n",
        "    max_encoder_length=context_length,\n",
        "    max_prediction_length=prediction_length,\n",
        "    time_varying_unknown_reals=['IPG2211A2N'],\n",
        "    categorical_encoders={\n",
        "        'series': NaNLabelEncoder(add_nan=True),\n",
        "        '__group_id__series': NaNLabelEncoder(add_nan=True)\n",
        "    }\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "fXNUOuPmY5rC",
        "outputId": "fd2c711f-72bf-4efb-fff2-a9738fb4d1ff"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e1dc32e9-76e7-4ebe-8396-421be031ad8b\" class=\"colab-df-container\">\n",
              "    <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>index</th>\n",
              "      <th>IPG2211A2N</th>\n",
              "      <th>time_idx</th>\n",
              "      <th>series</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01-21</td>\n",
              "      <td>431000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-01-28</td>\n",
              "      <td>379000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-02-04</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-02-11</td>\n",
              "      <td>577000</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-02-18</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>2021-10-17</td>\n",
              "      <td>1193000</td>\n",
              "      <td>195</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>2021-10-24</td>\n",
              "      <td>1018000</td>\n",
              "      <td>196</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>2021-10-31</td>\n",
              "      <td>821000</td>\n",
              "      <td>197</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>2021-11-07</td>\n",
              "      <td>1546000</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>2021-11-14</td>\n",
              "      <td>1129000</td>\n",
              "      <td>199</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 4 columns</p>\n",
              "</div>\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-e1dc32e9-76e7-4ebe-8396-421be031ad8b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e1dc32e9-76e7-4ebe-8396-421be031ad8b');\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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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5df4cdc8-2000-47db-b43d-064be8fa535e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         index  IPG2211A2N  time_idx  series\n",
              "0   2018-01-21      431000         0       1\n",
              "1   2018-01-28      379000         1       1\n",
              "2   2018-02-04           0         2       1\n",
              "3   2018-02-11      577000         3       1\n",
              "4   2018-02-18           0         4       1\n",
              "..         ...         ...       ...     ...\n",
              "195 2021-10-17     1193000       195       1\n",
              "196 2021-10-24     1018000       196       1\n",
              "197 2021-10-31      821000       197       1\n",
              "198 2021-11-07     1546000       198       1\n",
              "199 2021-11-14     1129000       199       1\n",
              "\n",
              "[200 rows x 4 columns]"
            ]
          },
          "execution_count": 108,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9wybWpj_Sx6h",
        "outputId": "03c86abf-857d-4947-a14a-86ff5eeae63f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "TimeSeriesDataSet[length=101](\n",
              "\ttime_idx='time_idx',\n",
              "\ttarget='IPG2211A2N',\n",
              "\tgroup_ids=['series'],\n",
              "\tweight=None,\n",
              "\tmax_encoder_length=60,\n",
              "\tmin_encoder_length=60,\n",
              "\tmin_prediction_idx=0,\n",
              "\tmin_prediction_length=20,\n",
              "\tmax_prediction_length=20,\n",
              "\tstatic_categoricals=[],\n",
              "\tstatic_reals=[],\n",
              "\ttime_varying_known_categoricals=[],\n",
              "\ttime_varying_known_reals=[],\n",
              "\ttime_varying_unknown_categoricals=[],\n",
              "\ttime_varying_unknown_reals=['IPG2211A2N'],\n",
              "\tvariable_groups={},\n",
              "\tconstant_fill_strategy={},\n",
              "\tallow_missing_timesteps=False,\n",
              "\tlags={},\n",
              "\tadd_relative_time_idx=False,\n",
              "\tadd_target_scales=False,\n",
              "\tadd_encoder_length=False,\n",
              "\ttarget_normalizer=NaNLabelEncoder(add_nan=False, warn=True),\n",
              "\tcategorical_encoders={'series': NaNLabelEncoder(add_nan=True, warn=True), '__group_id__series': NaNLabelEncoder(add_nan=True, warn=False)},\n",
              "\tscalers={},\n",
              "\trandomize_length=None,\n",
              "\tpredict_mode=False\n",
              ")"
            ]
          },
          "execution_count": 109,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "training_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "k715Ds13UdF_",
        "outputId": "812a93f6-096f-425b-ef4f-50d3997c08ab"
      },
      "outputs": [],
      "source": [
        "validation = TimeSeriesDataSet.from_dataset(training_dataset, train_data, min_prediction_idx=training_cutoff + 1)\n",
        "validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kBe7RPxpZpy3"
      },
      "outputs": [],
      "source": [
        "validation = TimeSeriesDataSet.from_dataset(training_dataset, train_data, min_prediction_idx=training_cutoff + 1)\n",
        "testing = TimeSeriesDataSet.from_dataset(training_dataset, test_data, predict=True, stop_randomization=True)\n",
        "\n",
        "batch_size = 64\n",
        "\n",
        "\n",
        "train_dataloader = training_dataset.to_dataloader(train=True, batch_size=batch_size, num_workers=0)\n",
        "val_dataloader = validation.to_dataloader(train=False, batch_size=batch_size, num_workers=0)\n",
        "test_dataloader = testing.to_dataloader(train=False, batch_size=1, shuffle=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n_kiMvmfT01H"
      },
      "source": [
        "## Train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aZgBkOxAxB8b"
      },
      "outputs": [],
      "source": [
        "import lightning.pytorch as pl\n",
        "from lightning.pytorch.callbacks import EarlyStopping\n",
        "import pandas as pd\n",
        "import torch\n",
        "\n",
        "from pytorch_forecasting import Baseline, NBeats, TimeSeriesDataSet\n",
        "from pytorch_forecasting.data import NaNLabelEncoder\n",
        "from pytorch_forecasting.data.examples import generate_ar_data\n",
        "from pytorch_forecasting.metrics import SMAPE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gsZVY8GhKfpe"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j5STQmgmw6uf",
        "outputId": "f48e5cd4-1089-44c8-ebd1-14eb56fa8cac"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/utilities/parsing.py:197: UserWarning: Attribute 'loss' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['loss'])`.\n",
            "  rank_zero_warn(\n",
            "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/utilities/parsing.py:197: UserWarning: Attribute 'logging_metrics' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['logging_metrics'])`.\n",
            "  rank_zero_warn(\n",
            "INFO: GPU available: False, used: False\n",
            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: False, used: False\n",
            "INFO: TPU available: False, using: 0 TPU cores\n",
            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO: IPU available: False, using: 0 IPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO: HPU available: False, using: 0 HPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "WARNING: Missing logger folder: /content/lightning_logs\n",
            "WARNING:lightning.pytorch.loggers.tensorboard:Missing logger folder: /content/lightning_logs\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "tensor(0.)"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# calculate baseline absolute error\n",
        "actuals = torch.cat([y[0] for x, y in iter(val_dataloader)])\n",
        "baseline_predictions = Baseline().predict(val_dataloader)\n",
        "SMAPE()(baseline_predictions, actuals)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xP-L99ewxUd2",
        "outputId": "82f09709-45b2-4abd-8ea4-af640c6e6695"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO: Global seed set to 42\n",
            "INFO:lightning.fabric.utilities.seed:Global seed set to 42\n",
            "INFO: GPU available: False, used: False\n",
            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: False, used: False\n",
            "INFO: TPU available: False, using: 0 TPU cores\n",
            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO: IPU available: False, using: 0 IPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO: HPU available: False, using: 0 HPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/utilities/parsing.py:197: UserWarning: Attribute 'loss' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['loss'])`.\n",
            "  rank_zero_warn(\n",
            "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/utilities/parsing.py:197: UserWarning: Attribute 'logging_metrics' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['logging_metrics'])`.\n",
            "  rank_zero_warn(\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_forecasting/models/nbeats/sub_modules.py:154: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:245.)\n",
            "  coefficients = torch.tensor([backcast_linspace**i for i in range(thetas_dim)], dtype=torch.float32)\n"
          ]
        }
      ],
      "source": [
        "pl.seed_everything(42)\n",
        "trainer = pl.Trainer(accelerator=\"auto\", gradient_clip_val=0.01)\n",
        "net = NBeats.from_dataset(training_dataset, learning_rate=0.0056234, weight_decay=1e-2, widths=[32, 512], backcast_loss_ratio=0.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697,
          "referenced_widgets": [
            "0a5da55a8030479ba76e03c5de6153d3",
            "6156f451140f4a03ad530a3f092b3641",
            "1ad388364a764e918a2fa6fe729cfbf8",
            "e8c85ec347c84353947794703cb7991b",
            "41020c32c87a4a12919fa899275f6109",
            "7cda330c07414f2db76283b95db4a16e",
            "69c2ef531b214e6881339c1ed1de85ce",
            "7bf89b92766e49309ca5ab72e9e1fa1d",
            "8e9c617aba3843b3965c39b5066aa353",
            "0e9c22a467e64f138af442b767cb3ed3",
            "a3be062f5d2b4a6985ca1963be6d5628"
          ]
        },
        "id": "ntyiZOMCxYMa",
        "outputId": "4839233d-c1b9-43bd-92e5-cbfc59207be4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/loops/utilities.py:72: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.\n",
            "  rank_zero_warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0a5da55a8030479ba76e03c5de6153d3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Finding best initial lr:   0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO: `Trainer.fit` stopped: `max_steps=100` reached.\n",
            "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_steps=100` reached.\n",
            "INFO: Learning rate set to 0.004466835921509632\n",
            "INFO:lightning.pytorch.tuner.lr_finder:Learning rate set to 0.004466835921509632\n",
            "INFO: Restoring states from the checkpoint path at /content/.lr_find_7597acd6-85d0-4bba-b4ed-f4cde2cab2ea.ckpt\n",
            "INFO:lightning.pytorch.utilities.rank_zero:Restoring states from the checkpoint path at /content/.lr_find_7597acd6-85d0-4bba-b4ed-f4cde2cab2ea.ckpt\n",
            "INFO: Restored all states from the checkpoint at /content/.lr_find_7597acd6-85d0-4bba-b4ed-f4cde2cab2ea.ckpt\n",
            "INFO:lightning.pytorch.utilities.rank_zero:Restored all states from the checkpoint at /content/.lr_find_7597acd6-85d0-4bba-b4ed-f4cde2cab2ea.ckpt\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "suggested learning rate: 0.004466835921509632\n"
          ]
        },
        {
          "data": {
            "image/png": 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WjUi6tgvjZ6zjzflpdG/RiEs7aMC8iLtR+BGHSjuYzz2fp5B2MB9Ps4kxF7UiJsQPH08zPl5m/Lw86dM6nBB/L6NLFRc1vHtTVu/O5vPle3hgxjp+GNeP2DB/o8sSEQdS+BGHmb3pAA/P3EB+STmRwT68e3MPElqEGV2WuKEn/9aRjftyWb8vl8Tpa5h5Tx98PD2MLktEHEQDKKTepR3MJ3HaGu75fA35JeX0ahXGD//op+AjhvHx9OCdm3sQ6u/Fhn25/PeHLUaXJCIOpPAj9WZ/ThGPfr2ega8v4qeNBzCZ4K6LWjHtzt5EBGlFZjFWs0b+TPx7N0wmmLZiD58tSze6JBFxEHV7SZ2yWm2s3n2U5LX7+SZlH6UVlTtsDzgvkocHtScuSusmifPo3yGCBwa05/9+3caT32/G29PM33s2N7osEalnCj9SJzZn5PL9ugx+WJ/Bgdxi+/t9WofzyOAO9GjeyMDqRE7uH5e1xVJUxke/7+Jf327E02xmREIzo8sSkXqk8CPn7IuVe5jw7Ub710E+ngzqHMWIHs24oHUYJpN2WxfnZTKZePyq8yirsDJl2W4e+Xo9nh4mhnVranRpIlJPFH7knBzMK+aFn/4A4PK4CK4/P5b+HZrg66WZM9JwmEwm/jO0E2VWG9NX7OHBr9bj5WHmyi7RRpcmIvVA4UfOSdL/tpJXUk58sxA+GHU+Hma18kjDZDKZeG5YZ8rKrcxM2cdDX63ngtbhhAV4G12aiNQxzfaSs7Zi5xGS1+7HZIJnh3VW8JEGz2w28eKIeDo3DaaorIJPft9ldEkiUg8UfuSslFVYefL7TQDc1Ks5XWNDjS1IpI54mE2Mu7QtAFOWpWMpKoXDhyE9vfK/Nm2IKtLQKfzIWZmyNJ1tWfk08vfi0UHaHFJcy8COUXQLsnHd799ga9sOmjSBVq0q/9uuHbzxBuTkGF2miJwlk82mf8b8lcViISQkhNzcXIKDtS7NX2XmFnP5awspKK3gpRFdtC6KuJ45cyi/5lrMRUUAmPnTX5NVsxf9/eGbb2DQIAMKFJGa1Pb3t1p+5Iw9+9MWCkor6N48lOsTYo0uR6RuzZkDV12FR0kxZmzVgw9UdnvZbFBUBFddVXm8iDQoCj9yRqav2MNPGw5gPjbI2axBzuJKcnJgxAiw2TBZrac+1mqtDEEjRqgLTKSBUfiRWkvZfZSnZ1UOcn54UAc6Nw0xuCKROjZlChQWVgab2rBaK4+fOrV+6xKROqXwI7VyMK+Y+6alUFZhY0jnKO69pI3RJYnULZsN3nrr7M59803NAhNpQBR+5LRKy60kTltDlqWEdhGBvHJ9V21ZIa7nyBHYsePMQ4zNVnlednb91CUidU7hR07r+Z+2sCr9KEE+nrx/awKBPloYXFxQfv65nZ+XVzd1iEi9U/iRU5q96QBTlu0GYOKN3WjdJNDgikTqSeA5/mwHBdVNHSJS7xR+5KRsNhtvzU8D4J5L2nD5eZEGVyRSj8LDoU2b4+v41JbJVHleWFj91CUidU7hR05qVfpRNmdY8PUyc/fFrY0uR6R+mUzwj3+c3bn333/moUlEDKPwIydVtanjtT2a0Ug7W4s7uO22ypWbzbX8q9Fsrjx+1Kj6rUtE6pTCj9Rob3Yhv2zJBOD2C1saW4yIo4SGVm5ZYTKdNgBZTSZsJhN8+23leSLSYCj8SI2mLE3HaoOL2jWmXaQGcoobGTQIfvoJ/PwqQ9BfurNsJhNWTBR5+jDvtU9h4ECDChWRs6U5y3KC/JJyZqzaC8Ad/VoZXI2IAQYNgn37KldufvPNynV8jjG1bs3qq2/mDjrB0RCSD+bTNkKzIEUaEkNbfiZNmkR8fDzBwcEEBwfTp08ffv75ZwCys7P5xz/+QYcOHfDz86N58+bcf//95ObmnvKao0ePxmQyVXsNHjzYEbfjMr5evZe8knJaNwngknZNjC5HxBihoZUDmbdvh8OHYdeuyv9u3073V5+mfftm5BWXc/vklRzJLzG6WhE5A4a2/DRr1owXX3yRdu3aYbPZmDJlCsOGDWPt2rXYbDYyMjJ49dVX6dixI7t37+aee+4hIyODr7/++pTXHTx4MJ9++qn9ax8fn/q+FZdhtdr4dGk6ALf3baWNS0VMpspp8OHh9rc8PUx8cGsC17y7lD3Zhdw5dTVf3HUBvl4eBhYqIrVlstmca0OasLAwXnnlFcaMGXPCZzNnzuSWW26hoKAAT8+ac9vo0aPJycnhu+++O+saLBYLISEh5ObmEhwcfNbXaYjmbsnizqmrCfb1ZPm/L8ffWz2jIiez41A+1767lNyiMq7qEs1bN3XXPxhEDFTb399OM+C5oqKCL7/8koKCAvr06VPjMVU3c7LgU2XhwoVERETQoUMH7r33Xo4cOXLK40tKSrBYLNVe7urjY9Pbb+rdXMFH5DTaNAnkvVsS8PIw8dPGA7zyS6rRJYlILRgefjZu3EhgYCA+Pj7cc889JCcn07FjxxOOO3z4MM8++yxjx4495fUGDx7M1KlTmTdvHi+99BKLFi1iyJAhVFRUnPScpKQkQkJC7K/Y2Nhzvq+GaFV6Nst2HsHDbGJUn5ZGlyPSIPRpE86L18YDMGnhDpLX7jO4IhE5HcO7vUpLS9mzZw+5ubl8/fXXfPTRRyxatKhaALJYLFxxxRWEhYUxa9YsvLy8an39nTt30qZNG+bOncvll19e4zElJSWUlBwfsGixWIiNjXWrbi+bzcbf31/OyvRsbuoVS9Kxv8xFpHZe+yWVt+anEejjyc//vIjYMH+jSxJxOw2m28vb25u2bduSkJBAUlISXbt25Y033rB/npeXx+DBgwkKCiI5OfmMgg9A69atady4MWlpaSc9xsfHxz7jrOrlbhZtO8TK9Gy8Pc3cf3k7o8sRaXD+eXk7Elo0Ir+knIe+Wk+F1amGU4o4jSxLMZm5xZSWWw2rwfDw81dWq9XeCmOxWBg4cCDe3t7MmjULX1/fM77evn37OHLkCNHR0XVdqsuwWm28MqdyrMKoC1oQHeJncEUiDY+nh5nXb+hGgLcHK9Oz+WDxTqNLEnFK//1xCxckzePz5bsNq8HQ8DNhwgQWL15Meno6GzduZMKECSxcuJCbb77ZHnwKCgr4+OOPsVgsZGZmkpmZWW38TlxcHMnJyQDk5+fzyCOPsHz5ctLT05k3bx7Dhg2jbdu2DBo0yKjbdHo/b8pkc4aFQB9P7ru0rdHliDRYzcP9efrqTgD836+pbNp/6nXJRNxRTmEpAI0Czqwnpy4ZOp3n4MGDjBo1igMHDhASEkJ8fDxz5szhiiuuYOHChaxYsQKAtm2r/0LetWsXLVu2BCA1NdW+8KGHhwcbNmxgypQp5OTkEBMTw8CBA3n22We11s9JlFdYee3XylafOy9qRZg2MBU5J9ef34x5W7OYszmLB2as44d/9NP6PyJ/crSgDIBQf+N+3xgafj7++OOTfta/f39qMxb7z8f4+fkxZ86cOqnNXXy7Zj87DxXQyN+LMdrKQuScmUwmkq6NZ82exWw/mM+LP2/lP0M7GV2WiNOwt/wYGH6cbsyPOE5JeQUT524DIPHStgT5GtcEKeJKwgK8efm6yhmTk5emsyD1oMEViTiPo4WVLT+N/I37naPw48bemLudjNxiooJ9ueWCFkaXI+JSLu0QwW19Kv9/9cjM9RzK0/5fIsVlFRSVVY7bNbLbS+HHTX3y+y7eXVi5U/W/hsRpTIJIPZhw5Xl0iAzicH4pD89cj1XT38XN5Rxr9fEwmwj2NW7kjcKPG/omZR///XELAA9d0Z7h3ZsaXJGIa/L18uDNm7rj42lm0bZDTD62abCIuzp6bLxPqJ8XJpNx++Ap/LiZX7dk8eg3GwAY068V4y7T1HaR+tQhKognrjoPgBd/3srmDE1/F/dlDz8GjvcBhR+3smzHERKnr6HCamNEj2Y8fuV5hiZvEXdxywUtGHBeJKUVVu7/Yi0FJeVGlyRiiBz7YGdjl1VR+HETxWUV3DcthdJyK1d0jOSlEV0wmxV8RBzBZDLx8nXxRAT5sONQAZe/tojpK/ZQVmHc8v4iRjje8qPwIw6QsvsoRwvLaBLkw1s3dcfTQ3/0Io4UFuDNpFsSiAnxJdNSzL+TNzLg/xbx3dr92gdM3EaOE0xzB4Uft/F72mEALmrXWDO7RAyS0KIRCx7pz3+u7kjjQG92Hylk/Ix1XP3W7xoLJG7h+NYWavkRB/h9+/HwIyLG8fH0YHTfVix+9FIeGdSBYF9PthywMOztJUycu+2ErrA/Dlh48eetvD1/O5biMoOqFqkbVQscGj3g2dDtLcQxjhaUsunYvyr7tlX4EXEG/t6eJF7alhvOj+XJ7zYxe3MmE+du59ctWTwztBN/HLDw1ep9bPzT5qifLknnkUEduP78WDw0Zk8aIGfY2gIUftzCkh2HsdmgQ2QQEUG+RpcjIn/SJMiHSbf04IcNB3jq+01szrBw3XvL7J97eZi4PC6SbQfz2HmogH99u5HPlu/m6as70atVmIGVi5w5Z9jaAhR+3EJVl1c/dXmJOCWTycTQrjFc0DqMJ5I38cuWLOKigrjh/FiGd29KWIA3peVWpi5L541529mcYeGG95dxZ79WTLjyPLUCSYPhLLO9FH5cnM1m4zeFH5EGISLIlw9Gnc/RglJC/auvgOvtaebOi1pzTfemvPrLNr5YuYePft9F+pFC3rixGwE++utcnF+Ok4z50YBnF7f7SCH7c4rw8jDRW03kIg1CowDvky5AGh7oQ9K1XXjzpu54e5qZ+0cWN7y/jMzcYgdXKXJmrFab04z5Ufhxcb8dm+Leo3kj/L31L0MRVzG0awxf3HUB4QHebM6wMOyd39m0X9PlxXnlFZdTtaSVWn6kXv2+/RCgKe4iriihRSO+S+xLu4hAsiwlXP/eMn5Yn2F0WSI1qhrv4+/tgY+nsevNKfy4sPIKK0t3HAGgX7smBlcjIvUhNsyfb+67kIvbN6GorIJ/fLGWl2Zv1arR4nSOOkmXFyj8uLSN+3PJKy4nxM+LLk1DjC5HROpJsK8Xn47uyd2XtAZg0sIdjJmyitwiLYoozsNZBjuDwo9Lq5rifmGbcE2FFXFxHmYTE4acxxs3dsPXy8zC1EMMf2cJa/ccNbo0EUAtP+IgVYOdtaqziPsY1q0pX99zIU1D/dh1uIBr3l3KvZ+nsPNQvtGliZtzlq0tQOHHZRWUlNv/xafBziLupXPTEGaN68t1Cc0wmeDnTZlc8fpiHk/eyME8TYkXYzjLNHdQ+HFZK3YdoazCRmyYHy3CA4wuR0QcLDzQh1ev78rsf17M5XERVFhtTFuxh4GvL2b93hyjyxM3dLzbSy0/Uk+W78wGoJ+6vETcWoeoID4e3ZMZYy+gY3QwOYVl3PzRClalZxtdmriZ491eavmRelK12Fm32FBjCxERp9C7dTgz7+nDBa3DyC8pZ9THK+2TIkQcwd7tFaCWH6kHNpuNzRkWADrFaIq7iFQK8PFk8u29uOTYmkB3TFnF/K1ZRpclbuJogVp+pB5l5BaTW1SGp9lEu8hAo8sRESfi6+XBB6MSGNQpktJyK3d/lsKCrQeNLkvcgAY8S73afKzLq11kkOFLiIuI8/Hx9ODtkT0Y2jWGsgobD3y1ThujSr2rGvOjAc9SL6q6vDpGBxtciYg4Ky8PM69e35UuTUPIKSzjgRnrtCWG1JvisgqKyioAdXtJPTk+3kfhR0ROztvTzBs3dsPf24NlO4/w/uIdRpckLqpqawsPs4lgX0+Dq1H4cUl/HFD4EZHaad0kkP8M7QTA//2yjXVaA0jqQdUaP6F+XphMxm+3pPDjYo4WlLI/pwiA8xR+RKQWrk9oxt/ioym32rj/i7Xkl5QbXZK4GHv4cYLxPqDw43K2HGv1aR7mT7Cvc/yQiYhzM5lMPH9NF5qG+rEnu5CnvtuEzabxP1J3cuyDnY0f7wMKPy5ni8b7iMhZCPHz4o0bu2E2wbdr9/PJknSjSxIXcrzlR+FH6sHmjMpp7go/InKmzm8ZxoQh5wHw3E9bmLtFCyBK3chxomnuoPDjcrSys4icizsvasVNvZpjs8H9X661/4NK5Fwc39pCLT9Sx4pKK9hxKB+Ajmr5EZGzYDKZ+O+wTvRr25jC0grunLKagxYtgCjn5vimpmr5kTqWmpWH1QaNA72JCPIxuhwRaaC8PMy8c3MP2jQJ4EBuMWOmrKawVDPA5Ow509YWoPDjUqqapzvGhDjFOgoi0nCF+HnxyeiehAV4s3F/Lv/9YYvRJUkD5kxbW4DCj0vRys4iUpdahAfwzsgeAHy5ai+r0rMNrkgaKs32+pNJkyYRHx9PcHAwwcHB9OnTh59//tn+eXFxMYmJiYSHhxMYGMiIESPIyjr17AObzcZTTz1FdHQ0fn5+DBgwgO3bt9f3rTgF7eklInWtT5twbuoVC8C/v91IabnV4IqkIdI6P3/SrFkzXnzxRVJSUli9ejWXXXYZw4YNY/PmzQA88MAD/PDDD8ycOZNFixaRkZHBtddee8prvvzyy7z55pu89957rFixgoCAAAYNGkRxsWsP2CuvsLJV21qISD14bHAc4QHebD+Yz4e/7TS6HGlgrFabfcyPBjwDV199NVdeeSXt2rWjffv2PP/88wQGBrJ8+XJyc3P5+OOP+b//+z8uu+wyEhIS+PTTT1m6dCnLly+v8Xo2m42JEyfyxBNPMGzYMOLj45k6dSoZGRl89913jr05B9t1uICScisB3h60DA8wuhwRcSGh/t488bfK9X/enLed3UcKDK5IGpK84nKsxxYMV/j5i4qKCr788ksKCgro06cPKSkplJWVMWDAAPsxcXFxNG/enGXLltV4jV27dpGZmVntnJCQEHr37n3ScwBKSkqwWCzVXg1NVZfXedHBmM0a7CwidWt4t6b0bRtOSbmVJ7/frO0vpNaqxvv4e3vg4+lhcDWVDA8/GzduJDAwEB8fH+655x6Sk5Pp2LEjmZmZeHt7ExoaWu34yMhIMjMza7xW1fuRkZG1PgcgKSmJkJAQ+ys2NvbcbsoAWtlZROqTyWTi2WGd8fY0s3jbIX7ccMDokqSBOOpk09zBCcJPhw4dWLduHStWrODee+/ltttuY8sWx06pnDBhArm5ufbX3r17Hfr960LVhqZa3FBE6kvrJoEk9m8LwDM/bCG7oNTgiqQhyHGyBQ7BCcKPt7c3bdu2JSEhgaSkJLp27cobb7xBVFQUpaWl5OTkVDs+KyuLqKioGq9V9f5fZ4Sd6hwAHx8f+4yzqldDYrPZtK2FiDjEPf1b0zYikMP5JTw8c726v+S01PJTC1arlZKSEhISEvDy8mLevHn2z1JTU9mzZw99+vSp8dxWrVoRFRVV7RyLxcKKFStOeo4r2H4wn5zCMrw9zbSLDDS6HBFxYT6eHrx1U3e8Pc3M33qQj3/fZXRJ4uScbWsLMDj8TJgwgcWLF5Oens7GjRuZMGECCxcu5OabbyYkJIQxY8bw4IMPsmDBAlJSUrj99tvp06cPF1xwgf0acXFxJCcnA5V90uPHj+e5555j1qxZbNy4kVGjRhETE8Pw4cMNusv6N3/rQQD6tA53msFkIuK6zosO5sm/dQTgpdlb2bAvx9iCxKk529YWAJ5GfvODBw8yatQoDhw4QEhICPHx8cyZM4crrrgCgNdffx2z2cyIESMoKSlh0KBBvPvuu9WukZqaSm7u8V2HH330UQoKChg7diw5OTn069eP2bNn4+vr69B7c6Sq8HNZXITBlYiIu7ild3OWph3m502ZjJu+lp/u70eQr/P8y16cx/FuL+f5+TDZ1GF7AovFQkhICLm5uU4//ie3sIwez/1KhdXGb49eSmyYv9EliYibyC0q48o3fmN/ThFXd43hzRu7aV9BOUHi9DX8tOEAT/2tI3f0a1Wv36u2v7+dbsyPnJnF2w9RYbXRLiJQwUdEHCrEz4u3RnbH02zih/UZGv8jNbJ3ewU4T8uPwk8Dt0BdXiJioB7NG/HIoA4APPfTH7z2S6pmgEk1RwuqBjw7z5gfhZ8GrMJqY+G2QwBcqvAjIgYZe3FrHryiPQBvzU/j38kbKa/QBqhSyRkHPCv8NGDr9+WQXVBKkK8nCS0aGV2OiLgpk8nE/Ze344VrumA2wRcr93LftDUUl1UYXZo4gaP2Hd3V7SV1oKrL6+L2TfDy0B+liBhrZO/mvHtzD7w9zfyyJYtbP15BlqXY6LLEQMVlFRQdC8Hq9pI6YZ/i3kFdXiLiHAZ3jmbK7b0I8vFkVfpRBk9czLw/sk5/orikqq0tPMwmgn0NXV2nGoWfBirLUszmDAsmE/Tv0MTockRE7Pq0Cee7cX3pGB3M0cIyxkxZzX9mbVY3mBs6nF8CQKifl1Mtg6Dw00BVdXl1bRZKeKCPwdWIiFTXpkkgyYkXckffynVdJi9NZ/g7S9h1uMDgysSRUnYfBaBDVJDBlVSn8NNAaVVnEXF2Pp4ePHV1Rz4d3ZPwAG+2ZuZx84fLycgpMro0cZDfth8GoF+7xgZXUp3CTwNUUl7B72mVP1AKPyLi7C6Ni+Dnf15EmyYBZOQWc9snK+3Tn8V1lVdYWb7zCAD92ir8yDlauSubwtIKIoJ86BTj3NtviIgARAT7MnVMb6KCfdl+MJ87Jq+iqFRjgFzZ+n055JeUE+rvRaeYEKPLqUbhpwGqakbs36GJUw0gExE5laahfkwd04sQPy/W7MnhvmkplGkxRJf1+/bKVp8L24TjYXau31UKPw3QlgwLULmsvIhIQ9I+MohPRp+Pr5eZBamHeOybDdoOw0X9nla5A0G/ts43I1nhpwHampkHQFy0urxEpOFJaBHGuzf3wMNs4ts1+7UhqgvKLyln7Z4cwPnG+4DCT4NzOL+Ew/klmEzQPjLQ6HJERM7KZXGRPH11RwBe/Hkrq9OzDa5I6tKKnUcot9poHuZP83B/o8s5gcJPA5N6rNWneZg//t7Os1qmiMiZuvWCFgztGkO51Ubi9DX2BfGk4auakexsU9yrKPw0MFVdXh0inWvBKBGRM2UymUi6tgttIwLJspRw/xdrqbBq/I8r+L1qfR8n7PIChZ8GJzWzcrCzxvuIiCsI8PHkvVt64O/twdIdR3j9121GlyTnKMtSzPaD+ZhMlTO9nJHCTwNjH+zsZEuFi4icrbYRQbw4Ih6AtxeksSD1oMEVSU12HS5g6rJ0CkrKT3lcVatPl6YhTrWT+58p/DQgFVYb27KOdXsp/IiICxnaNYZRfVoA8PT3mykt1/o/zibpf3/w1Peb+fsHy8iyFJ/0uCVpzt3lBQo/Dcqe7EKKy6z4eJppGR5gdDkiInXqX0PiaBzow57sQr5ctcfocuQvDh0bkL5pv4Xh7yzhjwOWE46x2WzHBzsr/Ehd2HrsB619ZJDTrZYpInKu/L09GT+gHQBvzttO/mm6V8Sx8osr/zyCfDw5kFvMdZOWsvAvXZTbD+ZzMK8EXy8zCS2ddyFezZVuQOwzvdTlJSIu6u89Y/n4913sOlzAR7/tZPyA9kaXJMdUhdFJtyTwzoI0lu08wh2TVzG0awyNArwJ8vFkx6ECAHq1CsfH08PIck9JLT8NSKoGO4uIi/PyMPPwwA4AfLh4J4fytPaPs6hq+WnayI8pd/TiuoRmWG3w3boMPl2Szpvz0/hp4wEA+rV1zlleVdTy04CkZlWFH01zFxHXdWWXKLo2C2H9vlzenr+dZ4Z1Nrokt2e12sgvrQw/gT6eeHuaeeW6eAZ2jCTtUD75xeXkl5STV1yOr5eZG3s1N7jiU1P4aSAKS8tJP1LZnKhuLxFxZSaTiceGxDHywxVMW7GHO/q1ooUmeRiqsKyCqv1ng3wro4PJZGJgpygGGljX2VK3VwOxPSsfmw3CA7xpEuRjdDkiIvXqwjaNuaR9E8qtNl79RQsfGq2qy8vTbMLHs+FHh4Z/B24iVYOdRcTNPDY4DpMJflifwYKtWvjQSPklZQAE+npiMjX82cYKPw3EH1XbWmi8j4i4iY4xwYy6oHLhw39+uZY9RwoNrsh95RUfH+/jChR+GgjN9BIRd/Tvq86jW2woluJy7v48haLSCqNLcktV09wVfsSh1O0lIu7Ix9ODSbf0IDzAmz8OWHj8u43YbNr53dHsCxz6KvyIgxzKK+FIQSkmU+XqziIi7iQ6xI+3RnbHbIJv1+zn8xXa+sLR8tTyI4629dh4n5bhAfh5O++KmSIi9eXCNo3515A4AP77w2bW7DlqcEXuparlJ9DXy+BK6obCTwNg7/JSq4+IuLG7LmrNlV2iKKuwcd/na7T6swNpzI84nPb0EhGpXFTv5eu60jYikExLMeOmr6G8wmp0WW6hKvxozI84TFW313nRCj8i4t4CfTx575YEAn08WbErmxd/3mp0SW5BU93FoSqsNrZn5QPQQWv8iIjQNiKQV6/vCsBHv+9i1voMgytyfer2Eoc6nF9CSbkVswmah/kbXY6IiFMY3DmKe/u3AeCxrzfYx0ZK/cgvPr7Csys4q/Czd+9e9u3bZ/965cqVjB8/ng8++KDOCpNKR/JLAQgL8MbD3PCXFBcRqSsPD+xAv7aNKSqr4O7PVpNbVGZ0SS7LPubHnVt+Ro4cyYIFCwDIzMzkiiuuYOXKlTz++OP897//rdMC3V12wfHwIyIix3mYTbx5U3eahvqRfqSQ8V+upcKqBRDrg33Mjzu3/GzatIlevXoB8NVXX9G5c2eWLl3KtGnTmDx5cq2vk5SURM+ePQkKCiIiIoLhw4eTmppq/zw9PR2TyVTja+bMmSe97ujRo084fvDgwWdzq4bLLqwMP438FX5ERP4qLMCb929NwMfTzILUQ0ycqx3g64PG/ABlZWX4+PgAMHfuXIYOHQpAXFwcBw4cqPV1Fi1aRGJiIsuXL+fXX3+lrKyMgQMHUlBQAEBsbCwHDhyo9nrmmWcIDAxkyJAhp7z24MGDq533xRdfnM2tGi47v3Idi/BAhR8RkZp0bhrCiyO6APDW/DRmb6r97yGpHVeb6n5Wd9GpUyfee+89rrrqKn799VeeffZZADIyMggPD6/1dWbPnl3t68mTJxMREUFKSgoXX3wxHh4eREVFVTsmOTmZG264gcDAwFNe28fH54RzG6Lswso+bLX8iIic3DXdm7Fxn4VPluzioa/W06ZJIO20MGydsNlsx1d49nHjFZ5feukl3n//ffr3789NN91E166VUw5nzZpl7w47G7m5uQCEhYXV+HlKSgrr1q1jzJgxp73WwoULiYiIoEOHDtx7770cOXLkpMeWlJRgsViqvZxFdsGxlh+N+REROaV/XxlHn9bhFJRWMPazFA2AriMl5VbKj42lcusxP/379+fw4cMcPnyYTz75xP7+2LFjee+9986qEKvVyvjx4+nbty+dO3eu8ZiPP/6Y8847jwsvvPCU1xo8eDBTp05l3rx5vPTSSyxatIghQ4ZQUVFR4/FJSUmEhITYX7GxsWd1D/VBA55FRGrH08PM2yMrB0DvOlzAwzPXawf4OlA12NlkAn8v19hf8qzCT1FRESUlJTRq1AiA3bt3M3HiRFJTU4mIiDirQhITE9m0aRNffvnlSb/n9OnTa9Xqc+ONNzJ06FC6dOnC8OHD+fHHH1m1ahULFy6s8fgJEyaQm5trf+3du/es7qE+VIWfRgo/IiKnFR7ow/u3JuDtYebXLVlMX6kd4M+VfbCztydmF1ly5azCz7Bhw5g6dSoAOTk59O7dm9dee43hw4czadKkM77euHHj+PHHH1mwYAHNmjWr8Zivv/6awsJCRo0adcbXb926NY0bNyYtLa3Gz318fAgODq72chZV4Sc8wMfgSkREGobOTUN4dHAHAJ79cQtpB7UA4rnId7Fp7nCW4WfNmjVcdNFFQGUoiYyMZPfu3UydOpU333yz1tex2WyMGzeO5ORk5s+fT6tWrU567Mcff8zQoUNp0qTJGde7b98+jhw5QnR09Bmfa7Tsgso+a3V7iYjU3h19W3FRu8YUl1m5/4t1lJTXPOxBTi+v5Njqzi4yzR3OMvwUFhYSFFQ5iv6XX37h2muvxWw2c8EFF7B79+5aXycxMZHPP/+c6dOnExQURGZmJpmZmRQVFVU7Li0tjcWLF3PnnXfWeJ24uDiSk5MByM/P55FHHmH58uWkp6czb948hg0bRtu2bRk0aNDZ3K5hrFYbRws15kdE5EyZzSZevb4rjfy92HLAwmu/aP2fs6WWn2Patm3Ld999x969e5kzZw4DBw4E4ODBg2fUZTRp0iRyc3Pp378/0dHR9teMGTOqHffJJ5/QrFkz+/f5q9TUVPtMMQ8PDzZs2MDQoUNp3749Y8aMISEhgd9++82+NlFDYSkus69W2ijANaYXiog4SmSwLy+NiAfgg8U7+X37YYMraphcbYFDOMt1fp566ilGjhzJAw88wGWXXUafPn2Aylag7t271/o6tR2F/8ILL/DCCy/U6jp+fn7MmTOn1jU4s6rxPkE+nvh4usYIexERRxrYKYqRvZszfcUeHvxqHb8+cAkh/vrH5JlwtQUO4Sxbfq677jr27NnD6tWrqwWNyy+/nNdff73OinN3muklInLunrjqPFo3DuBgXgmv/Zp6+hOkGvu+Xi7U8nNW4QcgKiqK7t27k5GRYd/hvVevXsTFxdVZce7uiNb4ERE5Z/7enjw3vHL9uM+X72ZzRq7BFTUsx7u9XKfF7KzCj9Vq5b///S8hISG0aNGCFi1aEBoayrPPPovVaq3rGt3WUYUfEZE6cWHbxlwVH43VBk99vxmrdn+vNQ14Pubxxx/n7bff5sUXX2Tt2rWsXbuWF154gbfeeosnn3yyrmt0W2r5ERGpO09cdR7+3h6k7D7Kt2v3G11Og2Ef8+Pu3V5Tpkzho48+4t577yU+Pp74+Hjuu+8+PvzwQyZPnlzHJbqvo/YFDhV+RETOVXSIH/df3g6ApP/9ob2/ailPLT+VsrOzaxzbExcXR3Z29jkXJZU04FlEpG7d0bcVbZoEcKSglNd/1do/tZGvRQ4rde3albfffvuE999++23i4+PPuSippG4vEZG65e1p5pmhlYOfpy5LZ0uGxeCKnJ99wLMLtfyc1Z28/PLLXHXVVcydO9e+xs+yZcvYu3cv//vf/+q0QHdWtbqzur1EROpOv3aNuapLND9tPMC4L9YwY2wfmgQ1rEVwHalqwLPbj/m55JJL2LZtG9dccw05OTnk5ORw7bXXsnnzZj777LO6rtFtHclXt5eISH146uqOxIT4svNQAbd8tMI+zEBOpJafP4mJieH555+v9t769ev5+OOP+eCDD865MFHLj4hIfYkM9mX6XRdww/vLSM3K49aPVzD9zgu0+nMNtMihOExxWQWFpZW7EKvlR0Sk7rVsHMD0uy6gcaA3mzMsjPp0JXnFmgH2Z6XlVkrKK9fvC3L3RQ6l/lUNdvbyMLlUP6uIiDNpGxHI53f2JtTfi/V7c7hj8iqKyyqMLstpFBzr8gII8HGdPSYVfpzUn1d3NplMBlcjIuK64qKC+XxMb4J8PVmVfpTnf/rD6JKcRtV4Hz8vDzw9XCcynFGTwrXXXnvKz3Nycs6lFvmTqpafRv7q8hIRqW+dm4bw9sge3PbJSj5bvpu+bcMZ3Dna6LIM54oLHMIZhp+QkJDTfj5q1KhzKkgqZReUABAeqPAjIuIIl7Rvwt2XtOb9RTt59OsNdIoJITbM3+iyDOWKW1vAGYafTz/9tL7qkL/ILqgcdBcWoLUnREQc5eGBHVixM5t1e3P455drmXF3H7xcqLvnTNlXd3axlh/3/RN1clUtP2Gadiki4jBeHmbeuqk7Qb6erNmTw/+5+RYYrjjNHRR+nJZafkREjBEb5s9LIyq3apq0cAeLtx0yuCLj2Bc4VPgRR7C3/GjMj4iIw13ZJZqbezcH4J9frmXf0UKDKzJGvosOeFb4cVJVS62HabaXiIghnvxbR7o0DeFoYRn3fJ7iluv/uOqAZ4UfJ5WtHd1FRAzl6+XBpFt6EBbgzab9Fh5P3oTNZjO6LIdy1anuCj9OSuFHRMR4zRr58/ZN3TGb4Js1+/hs+W6jS3Ko4wOeXWvyjcKPE6qw2sgpqhrwrPAjImKkC9s25l9D4gD47w9bWJWebXBFjqOp7uIwOYWlVLWsNtJUdxERw911UWv+Fh9NudXGvZ+vYW+2ewyA1pgfcZiqLq8QPy+X2ktFRKShMplMvHxdPHFRQRzOL2HkR8vJzC02uqx6l691fsRRqsJPuLq8RESchr+3J5Nv70XzMH/2Zhcx8qPlHMorMbqsepVXogHP4iAa7Cwi4pyiQnyZfldvYkJ82XmogFs/XsHRY39nuyK1/IjDZBce29Fd4UdExOk0a+TP9LsuICLIh62ZeYz6ZCWW4jKjy6oX9jE/avmR+padr24vERFn1rJxANPu7E1YgDcb9+dy55TVlJS71iKIFVYbhaWV96SWH6l3R9TtJSLi9NpFBvH5mN4E+Xiyclc2j329waUWQaxq9QGN+REHOFqo8CMi0hB0jAlm0i0JeJpNfLcug9fnbje6pDpTFX68Pcz4eHoYXE3dUvhxQhrwLCLScPRr15gXrukCwJvztvN1yj6DK6obrrqpKSj8OKUj+RrwLCLSkNzQM5bES9sAMOHbDSzdcdjgis6dfXVnFxvvAwo/Tqmq20sDnkVEGo6HrujA1V1jKKuwcfdnKaQdzDe6pHOS56LT3EHhx+nYbDYNeBYRaYDMZhOvXBfP+S0akVdczpgpqxr0GkD5LrrAISj8OJ3C0gpKy62Awo+ISEPj6+XB+7cm0KyRH7uPFHLP5yn2v9MbmqoxP662rxco/DidqsHOvl5m/L1d7wdORMTVhQf68PFtPQn08WTFrmyenrWpQU6BV8uPOMwR+75ePgZXIiIiZ6tDVBBv3dQdswm+WLmXT5akG13SGdOYH3GYqv7hRgFeBlciIiLn4tK4CB6/qiMAz/+0hQVbDxpc0ZlRy484zPHBzmr5ERFp6O7o25KbesVitcH4GevYn1NkdEm1pjE/9SQpKYmePXsSFBREREQEw4cPJzU1tdox/fv3x2QyVXvdc889p7yuzWbjqaeeIjo6Gj8/PwYMGMD27Q1j1c2jBZrmLiLiKkwmE88M7Uy32FByi8q4/4u1lFU0jAHQ9pYfhZ+6tWjRIhITE1m+fDm//vorZWVlDBw4kIKCgmrH3XXXXRw4cMD+evnll0953Zdffpk333yT9957jxUrVhAQEMCgQYMoLi6uz9upE1UtP438FX5ERFyBt6eZt27qTpCPJym7j/L6r9uMLqlW8uzdXq43DMPQODd79uxqX0+ePJmIiAhSUlK4+OKL7e/7+/sTFRVVq2vabDYmTpzIE088wbBhwwCYOnUqkZGRfPfdd9x44411dwP14Ki928v1fthERNxVbJg/L46IJ3H6GiYt2kGfNuFc1K6J0WWdUn6xVnh2iNzcXADCwsKqvT9t2jQaN25M586dmTBhAoWFhSe9xq5du8jMzGTAgAH290JCQujduzfLli2r8ZySkhIsFku1l1FyiirDT6hafkREXMpV8dGM7N0cmw0emLGeQ3klRpd0SlXdXkEa8Fx/rFYr48ePp2/fvnTu3Nn+/siRI/n8889ZsGABEyZM4LPPPuOWW2456XUyMzMBiIyMrPZ+ZGSk/bO/SkpKIiQkxP6KjY2tgzs6O0cLK5N2qL9afkREXM1Tf+tIh8ggDueX8MCMdU49/idfU93rX2JiIps2beLLL7+s9v7YsWMZNGgQXbp04eabb2bq1KkkJyezY8eOOvveEyZMIDc31/7au3dvnV37TOUUasyPiIir8vXy4O2R3fH1MvN72mGum7SUXYcLTn+iAfI01b1+jRs3jh9//JEFCxbQrFmzUx7bu3dvANLS0mr8vGpsUFZWVrX3s7KyTjpuyMfHh+Dg4Govo+So5UdExKW1iwzi3Zt7EOzryfp9uVz15m98tWqvU60CbbPZjnd7qeWnbtlsNsaNG0dycjLz58+nVatWpz1n3bp1AERHR9f4eatWrYiKimLevHn29ywWCytWrKBPnz51Und9sdlsfwo/avkREXFVl8VFMnv8xVzQOozC0goe/WYD901b4zQboRaWVlCVxdTyU8cSExP5/PPPmT59OkFBQWRmZpKZmUlRUeUiUDt27ODZZ58lJSWF9PR0Zs2axahRo7j44ouJj4+3XycuLo7k5GSgck2F8ePH89xzzzFr1iw2btzIqFGjiImJYfjw4UbcZq0VlVVQeqz/t5FafkREXFpMqB/T7ryAfw2Jw9Ns4udNmVz+f4v4YuUeKqzGtgJVtfqYTeDn5WFoLfXB0Dg3adIkoHIhwz/79NNPGT16NN7e3sydO5eJEydSUFBAbGwsI0aM4Iknnqh2fGpqqn2mGMCjjz5KQUEBY8eOJScnh379+jF79mx8fX3r/Z7ORdVgZ28Ps0v+sImISHUeZhP3XNKGvm0a89DMdWzLymfCtxuZvmIPzwzrRI/mjQyp68/7eplMJkNqqE8mmzN1MjoJi8VCSEgIubm5Dh3/szkjl6ve/J2IIB9WPj7g9CeIiIjLKKuwMnXZbib+us0+2HhEj2aMH9CO2DB/h9ayIPUgt3+6iqahfiz512UO/d7nora/v51iwLNU0mBnERH35eVhZky/Vsx/uD/XJVRO/vlmzT4ufXUhj369nt1HHDMr7HB+CY9/uxGA3q3DTnN0w6Tw40SOFmqBQxERd9ckyIdXr+9K8n0XclG7xpRbbXy1eh+XvbaIh75az54jJ1/o91yVV1gZN30NGbnFtG4cwH+Gdqq372UkhR8nUtXyo8HOIiLSvXkjPhvTm2/uvZBL2jehwmrjmzX7GDhxEe8v2kF5PSyQmPTzVpbvzCbA24MPRiUQ7IL7eoHCj1OpWuAw1E8tPyIiUimhRSOm3NGL7xL70qd1OMVlVpJ+3sqwd5awaX/u6S9QS9+v28/Hv+8C4LUbutI2IqjOru1sFH6ciH3MjzY1FRGRv+gWG8r0u3rzynXxhPh5sTnDwrB3lpD08x/nvE3GlgwLj32zAYDES9swuHPNa+m5CoUfJ2Lf10stPyIiUgOTycT158cy98FL+Ft8NBVWG+8v2sl909ZQUl5xVtessNp48Kt1FJdZuaR9Ex68okMdV+18FH6cyPF9vdTyIyIiJ9ckyIe3R/Zg0s098PY08+uWLO7+LIXisjMPQN+k7GNrZh7Bvp68/vdueJhdb12fv1L4cSI5RdraQkREam9Il2g+ua0nvl5mFqYe4s4pqyksLa/1+YWl5bz6SyoA91/ejrAA9/j9o/DjRI5PdVfLj4iI1E6/do2ZcnsvArw9+D3tMKM/XWXfnuJ0Ply8i4N5JcSG+XFrnxb1XKnzUPhxIrn2qe7ukbxFRKRu9G4dztQxvQny8WTlrmxGvLv0tDPBDuYV8/7iHQA8NjgOH0/32VZJ4cdJ2Gy2P3V7qeVHRETOTEKLRky7qzeNA71Jzcpj+DtLeGPu9pPOBHv91+0UllbQLTaUq7q49uyuv1L4cRKW4nL7Lr4hfgo/IiJy5uKbhTJn/MUM6RxFudXG63O3ce27S0nNzKt23LasPGas2gPAE1ed55Kbl56Kobu6y3FVXV5+Xh74akd3ERE5S+GBPrx7cw9mrc/gqe83s3F/LoMmLqZpqB/tIwNpHxVESvpRrDYY0jmK81u65v5dp6Lw4ySOapq7iIjUEZPJxLBuTbmgdTiPJ29i7h9Z7M8pYn9OEQtSDwHgaTbx2OA4gys1hsKPk9A0dxERqWuRwb58dNv55BSWsi0rn9SsPLZn5bHzUAGDO0fRsnGA0SUaQuHHSeRomruIiNSTUH9verUKo1cr9+viqokGPDuJowVV3V5q+REREalPCj9OoqrbK0QtPyIiIvVK4cdJ5NgXOFT4ERERqU8KP07i+Kam6vYSERGpTwo/TuLosZYfLXAoIiJSvxR+nIRafkRERBxD4cdJaF8vERERx1D4cRJVU921yKGIiEj9UvhxAhVWG5bickAtPyIiIvVN4ccJ5B7r8gII1YBnERGReqXw4wSqBjsH+Xri6aE/EhERkfqk37ROoGqau7q8RERE6p/CjxPQNHcRERHHUfhxAjla4FBERMRhFH6cwFG1/IiIiDiMwo8TqJrtpU1NRURE6p/CjxOoavkJUcuPiIhIvVP4cQJVs73U8iMiIlL/FH6cQK6muouIiDiMwo8TqOr20r5eIiIi9U/hxwnk2Lu9FH5ERETqm8KPE6ha5FD7eomIiNQ/hR+DlZZbKSitANTyIyIi4ggKPwbLKaps9TGbKjc2FRERkfplaPhJSkqiZ8+eBAUFERERwfDhw0lNTbV/np2dzT/+8Q86dOiAn58fzZs35/777yc3N/eU1x09ejQmk6naa/DgwfV9O2flz1tbmM0mg6sRERFxfYaGn0WLFpGYmMjy5cv59ddfKSsrY+DAgRQUFACQkZFBRkYGr776Kps2bWLy5MnMnj2bMWPGnPbagwcP5sCBA/bXF198Ud+3c1Zy7NPc1eUlIiLiCIb2s8yePbva15MnTyYiIoKUlBQuvvhiOnfuzDfffGP/vE2bNjz//PPccsstlJeX4+l58vJ9fHyIioqqt9rryvFp7hrsLCIi4ghONeanqjsrLCzslMcEBwefMvgALFy4kIiICDp06MC9997LkSNHTnpsSUkJFoul2stRcrSpqYiIiEM5TfixWq2MHz+evn370rlz5xqPOXz4MM8++yxjx4495bUGDx7M1KlTmTdvHi+99BKLFi1iyJAhVFRU1Hh8UlISISEh9ldsbOw5309t2bu9NM1dRETEIUw2m81mdBEA9957Lz///DO///47zZo1O+Fzi8XCFVdcQVhYGLNmzcLLq/ZhYefOnbRp04a5c+dy+eWXn/B5SUkJJSUl1b5XbGysvZWpPr3481beW7SDO/q24qmrO9br9xIREXFlFouFkJCQ0/7+doqWn3HjxvHjjz+yYMGCGoNPXl4egwcPJigoiOTk5DMKPgCtW7emcePGpKWl1fi5j48PwcHB1V6OklukMT8iIiKOZGj4sdlsjBs3juTkZObPn0+rVq1OOMZisTBw4EC8vb2ZNWsWvr6+Z/x99u3bx5EjR4iOjq6LsuvU0QLt6C4iIuJIhoafxMREPv/8c6ZPn05QUBCZmZlkZmZSVFQEHA8+BQUFfPzxx1gsFvsxfx6/ExcXR3JyMgD5+fk88sgjLF++nPT0dObNm8ewYcNo27YtgwYNMuQ+T0WbmoqIiDiWoVPdJ02aBED//v2rvf/pp58yevRo1qxZw4oVKwBo27ZttWN27dpFy5YtAUhNTbXPFPPw8GDDhg1MmTKFnJwcYmJiGDhwIM8++yw+Pj71e0NnIbeoap0ftfyIiIg4gqHh53Rjrfv373/aY/56HT8/P+bMmXPOtTnKUU11FxERcSinGPDszv68vYWIiIjUP4UfAxWVVlBSbgWgUYBafkRERBxB4cdAVTu6e5pNBHh7GFyNiIiIe1D4MVDVNPdQf29MJu3oLiIi4ggKPwY6vq+XxvuIiIg4isKPgXI0zV1ERMThFH4MpAUORUREHE/hx0Da0V1ERMTxFH4MZB/zo2nuIiIiDqPwY6CjhRrzIyIi4mgKPwY63u2llh8RERFHUfgxkKa6i4iIOJ7Cj4GqprqHKPyIiIg4jMKPgXK0o7uIiIjDKfwYxGazHR/zo5YfERERh1H4MUh+STnlVhuglh8RERFHUvgxSFWrj6+XGV8v7eguIiLiKAo/BtE0dxEREWMo/Bjk+L5eGu8jIiLiSAo/BlH4ERERMYbCj0Fyj63xo8HOIiIijqXwY5CjBVXT3BV+REREHEnhxyA5Rer2EhERMYLCj0GqZntpXy8RERHHUvgxSNXWFprqLiIi4lgKPwY5qq0tREREDKHwYxD7pqYBavkRERFxJIUfg+QUVa3wrJYfERERR1L4MUCF1WZf50dT3UVERBxL4ccAecVl2Co3dCdELT8iIiIOpfBjgKrBzoE+nnh76o9ARETEkfSb1wBV+3qp1UdERMTxFH4MkFu1wGGAwo+IiIijKfwYoKrlR5uaioiIOJ7CjwGqtrZQt5eIiIjjKfwYIEctPyIiIoZR+DGAtrYQERExjsKPAXK0wKGIiIhhFH4McLzbSy0/IiIijqbwY4AcdXuJiIgYxtDwk5SURM+ePQkKCiIiIoLhw4eTmppa7Zji4mISExMJDw8nMDCQESNGkJWVdcrr2mw2nnrqKaKjo/Hz82PAgAFs3769Pm/ljFRNdVe3l4iIiOMZGn4WLVpEYmIiy5cv59dff6WsrIyBAwdSUFBgP+aBBx7ghx9+YObMmSxatIiMjAyuvfbaU1735Zdf5s033+S9995jxYoVBAQEMGjQIIqLi+v7lmrF3vKjqe4iIiIOZ7LZqrbYNN6hQ4eIiIhg0aJFXHzxxeTm5tKkSROmT5/OddddB8DWrVs577zzWLZsGRdccMEJ17DZbMTExPDQQw/x8MMPA5Cbm0tkZCSTJ0/mxhtvPG0dFouFkJAQcnNzCQ4OrtN7LKuw0u7xnwFY++QVNApQ64+IiEhdqO3vb6ca85ObmwtAWFgYACkpKZSVlTFgwAD7MXFxcTRv3pxly5bVeI1du3aRmZlZ7ZyQkBB69+590nNKSkqwWCzVXvWlqtXHZIJgtfyIiIg4nNOEH6vVyvjx4+nbty+dO3cGIDMzE29vb0JDQ6sdGxkZSWZmZo3XqXo/MjKy1uckJSUREhJif8XGxp7j3ZxcblHleJ9gXy88zKZ6+z4iIiJSM6cJP4mJiWzatIkvv/zS4d97woQJ5Obm2l979+6tt+9VtcChprmLiIgYwynCz7hx4/jxxx9ZsGABzZo1s78fFRVFaWkpOTk51Y7PysoiKiqqxmtVvf/XGWGnOsfHx4fg4OBqr/pytKCy5SdEM71EREQMYWj4sdlsjBs3juTkZObPn0+rVq2qfZ6QkICXlxfz5s2zv5eamsqePXvo06dPjdds1aoVUVFR1c6xWCysWLHipOc4UtXqzmr5ERERMYah4ScxMZHPP/+c6dOnExQURGZmJpmZmRQVFQGVA5XHjBnDgw8+yIIFC0hJSeH222+nT58+1WZ6xcXFkZycDIDJZGL8+PE899xzzJo1i40bNzJq1ChiYmIYPny4EbdZTdXqzprmLiIiYgxPI7/5pEmTAOjfv3+19z/99FNGjx4NwOuvv47ZbGbEiBGUlJQwaNAg3n333WrHp6am2meKATz66KMUFBQwduxYcnJy6NevH7Nnz8bX17de76c2jq/urG4vERERIzjVOj/Ooj7X+Znw7Ua+WLmHBwa0558D2tXptUVERNxZg1znxx1UTXXXvl4iIiLGUPhxsKMF2tRURETESAo/DqZNTUVERIyl8ONguZrqLiIiYiiFHweravlppJYfERERQyj8OFBxWQXFZVYAQtTyIyIiYgiFHweqWuPHw2wiyMfQJZZERETclsKPAx390+rOJpN2dBcRETGCwo8DHV/dWV1eIiIiRlH4caAcTXMXERExnMKPA2lHdxEREeMp/DiQFjgUERExnsKPA9nH/Pip5UdERMQoCj8OVDXmp1GAWn5ERESMosVmHOjm3i3o1SqcjtHBRpciIiLithR+HKhrbChdY0ONLkNERMStqdtLRERE3IrCj4iIiLgVhR8RERFxKwo/IiIi4lYUfkRERMStKPyIiIiIW1H4EREREbei8CMiIiJuReFHRERE3IrCj4iIiLgVhR8RERFxKwo/IiIi4lYUfkRERMStaFf3GthsNgAsFovBlYiIiEhtVf3ervo9fjIKPzXIy8sDIDY21uBKRERE5Ezl5eUREhJy0s9NttPFIzdktVrJyMggKCiIXr16sWrVqhOO6dmzZ63e//PXFouF2NhY9u7dS3BwcP3dQC3qrI9zT3f82X5e0/une8/Rz/pcnvOZnl+bY091jJ6znvPp6DnrOdf2M2f7XWiz2cjLyyMmJgaz+eQje9TyUwOz2UyzZs0A8PDwqPEPp7bv13RccHCww8LPyeqsj3NPd/zZfl7T+7V9z1HP+lye85meX5tjT3WMnrOe8+noOes51/YzZ/xdeKoWnyoa8HwaiYmJ5/T+yY5zlHP5/md67umOP9vPa3q/tu85yrl+7zM5vzbHnuoYPee6O1bP+dzP13N2zPmOfM41vW/078K/UreXA1ksFkJCQsjNzXVYy4+70rN2DD1nx9Bzdgw9Z8dwhueslh8H8vHx4emnn8bHx8foUlyenrVj6Dk7hp6zY+g5O4YzPGe1/IiIiIhbUcuPiIiIuBWFHxEREXErCj8iIiLiVhR+RERExK0o/IiIiIhbUfhxYi1btiQ+Pp5u3bpx6aWXGl2OSyssLKRFixY8/PDDRpfiknJycjj//PPp1q0bnTt35sMPPzS6JJe0d+9e+vfvT8eOHYmPj2fmzJlGl+TSrrnmGho1asR1111ndCku5ccff6RDhw60a9eOjz76qF6+h6a6O7GWLVuyadMmAgMDjS7F5T3++OOkpaURGxvLq6++anQ5LqeiooKSkhL8/f0pKCigc+fOrF69mvDwcKNLcykHDhwgKyuLbt26kZmZSUJCAtu2bSMgIMDo0lzSwoULycvLY8qUKXz99ddGl+MSysvL6dixIwsWLCAkJISEhASWLl1a539XqOVH3N727dvZunUrQ4YMMboUl+Xh4YG/vz8AJSUl2Gw29O+uuhcdHU23bt0AiIqKonHjxmRnZxtblAvr378/QUFBRpfhUlauXEmnTp1o2rQpgYGBDBkyhF9++aXOv4/Cz1lavHgxV199NTExMZhMJr777rsTjnnnnXdo2bIlvr6+9O7dm5UrV57R9zCZTFxyySX07NmTadOm1VHlDYsjnvPDDz9MUlJSHVXcMDniOefk5NC1a1eaNWvGI488QuPGjeuo+obDEc+5SkpKChUVFcTGxp5j1Q2TI5+1HHeuzz0jI4OmTZvav27atCn79++v8zoVfs5SQUEBXbt25Z133qnx8xkzZvDggw/y9NNPs2bNGrp27cqgQYM4ePCg/Ziq8Q9/fWVkZADw+++/k5KSwqxZs3jhhRfYsGGDQ+7NmdT3c/7+++9p37497du3d9QtOSVH/DyHhoayfv16du3axfTp08nKynLIvTkTRzxngOzsbEaNGsUHH3xQ7/fkrBz1rKW6unjuDmGTcwbYkpOTq73Xq1cvW2Jiov3riooKW0xMjC0pKemsvsfDDz9s+/TTT8+hyoavPp7zv/71L1uzZs1sLVq0sIWHh9uCg4NtzzzzTF2W3eA44uf53nvvtc2cOfNcymzw6us5FxcX2y666CLb1KlT66rUBq8+f6YXLFhgGzFiRF2U6XLO5rkvWbLENnz4cPvn//znP23Tpk2r89rU8lMPSktLSUlJYcCAAfb3zGYzAwYMYNmyZbW6RkFBAXl5eQDk5+czf/58OnXqVC/1NlR18ZyTkpLYu3cv6enpvPrqq9x111089dRT9VVyg1QXzzkrK8v+85ybm8vixYvp0KFDvdTbUNXFc7bZbIwePZrLLruMW2+9tb5KbfDq4lnLmavNc+/VqxebNm1i//795Ofn8/PPPzNo0KA6r8Wzzq8oHD58mIqKCiIjI6u9HxkZydatW2t1jaysLK655hqgcqbMXXfdRc+ePeu81oasLp6znF5dPOfdu3czduxY+0Dnf/zjH3Tp0qU+ym2w6uI5L1myhBkzZhAfH28fa/HZZ5/pWf9FXf3dMWDAANavX09BQQHNmjVj5syZ9OnTp67LdRm1ee6enp689tprXHrppVitVh599NF6mRWq8OOkWrduzfr1640uw62MHj3a6BJcVq9evVi3bp3RZbi8fv36YbVajS7DbcydO9foElzS0KFDGTp0aL1+D3V71YPGjRvj4eFxwoDOrKwsoqKiDKrK9eg5O4aes2PoOTuOnrUxnOm5K/zUA29vbxISEpg3b579PavVyrx589QkWof0nB1Dz9kx9JwdR8/aGM703NXtdZby8/NJS0uzf71r1y7WrVtHWFgYzZs358EHH+S2227j/PPPp1evXkycOJGCggJuv/12A6tuePScHUPP2TH0nB1Hz9oYDea51/n8MTexYMECG3DC67bbbrMf89Zbb9maN29u8/b2tvXq1cu2fPly4wpuoPScHUPP2TH0nB1Hz9oYDeW5a28vERERcSsa8yMiIiJuReFHRERE3IrCj4iIiLgVhR8RERFxKwo/IiIi4lYUfkRERMStKPyIiIiIW1H4EREREbei8CMiLqlly5ZMnDjR6DJExAlphWcROWujR48mJyeH7777zuhSTnDo0CECAgLw9/c3upQaOfOzE3F1avkRkQalrKysVsc1adLEkOBT2/pExDgKPyJSbzZt2sSQIUMIDAwkMjKSW2+9lcOHD9s/nz17Nv369SM0NJTw8HD+9re/sWPHDvvn6enpmEwmZsyYwSWXXIKvry/Tpk1j9OjRDB8+nFdffZXo6GjCw8NJTEysFjz+2u1lMpn46KOPuOaaa/D396ddu3bMmjWrWr2zZs2iXbt2+Pr6cumllzJlyhRMJhM5OTknvUeTycSkSZMYOnQoAQEBPP/881RUVDBmzBhatWqFn58fHTp04I033rCf85///IcpU6bw/fffYzKZMJlMLFy4EIC9e/dyww03EBoaSlhYGMOGDSM9Pf3s/gBEpEYKPyJSL3Jycrjsssvo3r07q1evZvbs2WRlZXHDDTfYjykoKODBBx9k9erVzJs3D7PZzDXXXIPVaq12rX/961/885//5I8//mDQoEEALFiwgB07drBgwQKmTJnC5MmTmTx58ilreuaZZ7jhhhvYsGEDV155JTfffDPZ2dkA7Nq1i+uuu47hw4ezfv167r77bh5//PFa3et//vMfrrnmGjZu3Mgdd9yB1WqlWbNmzJw5ky1btvDUU0/x73//m6+++gqAhx9+mBtuuIHBgwdz4MABDhw4wIUXXkhZWRmDBg0iKCiI3377jSVLlhAYGMjgwYMpLS2t7aMXkdNx+D7yIuIybrvtNtuwYcNq/OzZZ5+1DRw4sNp7e/futQG21NTUGs85dOiQDbBt3LjRZrPZbLt27bIBtokTJ57wfVu0aGErLy+3v3f99dfb/v73v9u/btGihe3111+3fw3YnnjiCfvX+fn5NsD2888/22w2m+2xxx6zde7cudr3efzxx22A7ejRozU/gGPXHT9+/Ek/r5KYmGgbMWJEtXv467P77LPPbB06dLBZrVb7eyUlJTY/Pz/bnDlzTvs9RKR21PIjIvVi/fr1LFiwgMDAQPsrLi4OwN61tX37dm666SZat25NcHAwLVu2BGDPnj3VrnX++eefcP1OnTrh4eFh/zo6OpqDBw+esqb4+Hj7/w4ICCA4ONh+TmpqKj179qx2fK9evWp1rzXV984775CQkECTJk0IDAzkgw8+OOG+/mr9+vWkpaURFBRkf2ZhYWEUFxdX6w4UkXPjaXQBIuKa8vPzufrqq3nppZdO+Cw6OhqAq6++mhYtWvDhhx8SExOD1Wqlc+fOJ3TxBAQEnHANLy+val+bTKYTusvq4pza+Gt9X375JQ8//DCvvfYaffr0ISgoiFdeeYUVK1ac8jr5+fkkJCQwbdq0Ez5r0qTJOdcpIpUUfkSkXvTo0YNvvvmGli1b4ul54l81R44cITU1lQ8//JCLLroIgN9//93RZdp16NCB//3vf9XeW7Vq1Vlda8mSJVx44YXcd9999vf+2nLj7e1NRUVFtfd69OjBjBkziIiIIDg4+Ky+t4icnrq9ROSc5Obmsm7dumqvvXv3kpiYSHZ2NjfddBOrVq1ix44dzJkzh9tvv52KigoaNWpEeHg4H3zwAWlpacyfP58HH3zQsPu4++672bp1K4899hjbtm3jq6++sg+gNplMZ3Stdu3asXr1aubMmcO2bdt48sknTwhSLVu2ZMOGDaSmpnL48GHKysq4+eabady4McOGDeO3335j165dLFy4kPvvv599+/bV1a2KuD2FHxE5JwsXLqR79+7VXs888wwxMTEsWbKEiooKBg4cSJcuXRg/fjyhoaGYzWbMZjNffvklKSkpdO7cmQceeIBXXnnFsPto1aoVX3/9Nd9++y3x8fFMmjTJPtvLx8fnjK519913c+211/L3v/+d3r17c+TIkWqtQAB33XUXHTp04Pzzz6dJkyYsWbIEf39/Fi9eTPPmzbn22ms577zzGDNmDMXFxWoJEqlDWuFZROQknn/+ed577z327t1rdCkiUoc05kdE5Jh3332Xnj17Eh4ezpIlS3jllVcYN26c0WWJSB1T+BEROWb79u0899xzZGdn07x5cx566CEmTJhgdFkiUsfU7SUiIiJuRQOeRURExK0o/IiIiIhbUfgRERERt6LwIyIiIm5F4UdERETcisKPiIiIuBWFHxEREXErCj8iIiLiVhR+RERExK38P8Lnp44LfrXKAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# find optimal learning rate\n",
        "from lightning.pytorch.tuner import Tuner\n",
        "\n",
        "res = Tuner(trainer).lr_find(net, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader, min_lr=1e-5)\n",
        "print(f\"suggested learning rate: {res.suggestion()}\")\n",
        "fig = res.plot(show=True, suggest=True)\n",
        "fig.show()\n",
        "net.hparams.learning_rate = res.suggestion()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694,
          "referenced_widgets": [
            "1b5559a89e4448a2b09bd4768b6d9b0c",
            "5eba228b38d64727beb83de7b3acff1b",
            "1b5d6fad9c7a4bcf9a700fc02b8bab6f",
            "a2f851ea2d784c12b4fd36d099035ade",
            "ed5dca9b49824fd7b449aefd8a232c8e",
            "36c3b7e7cd004a4e858088def2c8857d",
            "309eaa52e7d34e2a82da1a0f15223fe7",
            "6758cb61bdbb43baae59d5101fe68f35",
            "c96c6c13003e436399c67805693dba55",
            "edb71a99554541d28be03a2131d0dc08",
            "00cd3a95ba044533baa40543109e7774",
            "90accdc43b9f4b4181efb9140a77a0d2",
            "46548ce566e04213a9c5761ce0c0a518",
            "2a1095cf12804137a7c4cb36c3c09ecb",
            "20f06844c0c8492ab8afb4ebcec8fc4e",
            "ed73d0ee254047bd9d17bff865f80e7e",
            "b37224ec60e94f7cbd8f4c4613f198d7",
            "d6639581b8fa4cd8b363f23223fd3334",
            "bdd7e640eda74c4ebe24c7ffe7247df0",
            "0e1966e9326a4f039e2df7105b2a509f",
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            "809b7a33394f472b8f1805eaa3ad1e6f",
            "8b814148adec4f92b5a5703eb4d29130",
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            "f5560a82b466470695893482b50344b3",
            "058685b18af549528db5fdf9338ee528",
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            "4eec3c7676a34e13bd1b50b94b14fc61",
            "2031d03480ed402c82aa684489b9efe4",
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            "d993fc2706bf4845ab836038913c3d69",
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            "a022fd8980574472bda8507edac53547",
            "054bfeca8e854832aa4324b4aa7addb7",
            "27465caafc27466a84432320d8b730e0",
            "be147f4aa4844658b94bad40d499d4e9",
            "17f79bbee42943e381c5b79bf73fa655",
            "52c1e4a4e81c4eb48db7a724d534b239",
            "7525cb8d2bf149989186546e623a11c7",
            "c6d29e112c634c1fb0018266473242d7",
            "37cb000b5d8b46e4b9d490701ca4422b",
            "97ec1794990846c48d1825c2170a2eff",
            "a2634b9ff1364f93860b604ed2d68c5e",
            "db6bcedfa5c444168c1dae0bd1306aeb"
          ]
        },
        "id": "ru8GScDtxYJF",
        "outputId": "f192db82-7194-40e0-a1b2-f0c87021012f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO: GPU available: False, used: False\n",
            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: False, used: False\n",
            "INFO: TPU available: False, using: 0 TPU cores\n",
            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO: IPU available: False, using: 0 IPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO: HPU available: False, using: 0 HPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/utilities/parsing.py:197: UserWarning: Attribute 'loss' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['loss'])`.\n",
            "  rank_zero_warn(\n",
            "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/utilities/parsing.py:197: UserWarning: Attribute 'logging_metrics' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['logging_metrics'])`.\n",
            "  rank_zero_warn(\n",
            "INFO: \n",
            "  | Name            | Type       | Params\n",
            "-----------------------------------------------\n",
            "0 | loss            | MASE       | 0     \n",
            "1 | logging_metrics | ModuleList | 0     \n",
            "2 | net_blocks      | ModuleList | 1.7 M \n",
            "-----------------------------------------------\n",
            "1.7 M     Trainable params\n",
            "0         Non-trainable params\n",
            "1.7 M     Total params\n",
            "6.876     Total estimated model params size (MB)\n",
            "INFO:lightning.pytorch.callbacks.model_summary:\n",
            "  | Name            | Type       | Params\n",
            "-----------------------------------------------\n",
            "0 | loss            | MASE       | 0     \n",
            "1 | logging_metrics | ModuleList | 0     \n",
            "2 | net_blocks      | ModuleList | 1.7 M \n",
            "-----------------------------------------------\n",
            "1.7 M     Trainable params\n",
            "0         Non-trainable params\n",
            "1.7 M     Total params\n",
            "6.876     Total estimated model params size (MB)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1b5559a89e4448a2b09bd4768b6d9b0c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Sanity Checking: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "90accdc43b9f4b4181efb9140a77a0d2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "809b7a33394f472b8f1805eaa3ad1e6f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2031d03480ed402c82aa684489b9efe4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "054bfeca8e854832aa4324b4aa7addb7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO: `Trainer.fit` stopped: `max_epochs=3` reached.\n",
            "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=3` reached.\n"
          ]
        }
      ],
      "source": [
        "early_stop_callback = EarlyStopping(monitor=\"val_loss\", min_delta=1e-4, patience=10, verbose=False, mode=\"min\")\n",
        "trainer = pl.Trainer(\n",
        "    max_epochs=3,\n",
        "    accelerator=\"auto\",\n",
        "    enable_model_summary=True,\n",
        "    gradient_clip_val=0.01,\n",
        "    callbacks=[early_stop_callback],\n",
        "    limit_train_batches=150,\n",
        ")\n",
        "\n",
        "import time\n",
        "start_time = time.time()\n",
        "net = NBeats.from_dataset(\n",
        "    training_dataset,\n",
        "    learning_rate=0.004466835921509632,\n",
        "    log_interval=10,\n",
        "    log_val_interval=1,\n",
        "    weight_decay=1e-2,\n",
        "    widths=[32, 512],\n",
        "    backcast_loss_ratio=1.0,\n",
        ")\n",
        "\n",
        "trainer.fit(\n",
        "    net,\n",
        "    train_dataloaders=train_dataloader,\n",
        "    val_dataloaders=val_dataloader,\n",
        ")\n",
        "\n",
        "end_time = time.time()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zAyRV_jCTspJ"
      },
      "source": [
        "## Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0w6Q11OU0inB"
      },
      "outputs": [],
      "source": [
        "best_model_path = trainer.checkpoint_callback.best_model_path\n",
        "best_model = NBeats.load_from_checkpoint(best_model_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GeG8-qmD5Imb",
        "outputId": "d194d9ed-efe2-4878-8a84-e825c946bb10"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "NBeats(\n",
              "  \t\"backcast_loss_ratio\":           1.0\n",
              "  \t\"context_length\":                60\n",
              "  \t\"dropout\":                       0.1\n",
              "  \t\"expansion_coefficient_lengths\": [3, 7]\n",
              "  \t\"learning_rate\":                 0.004466835921509632\n",
              "  \t\"log_gradient_flow\":             False\n",
              "  \t\"log_interval\":                  10\n",
              "  \t\"log_val_interval\":              1\n",
              "  \t\"logging_metrics\":               ModuleList(\n",
              "  \t  (0): SMAPE()\n",
              "  \t  (1): MAE()\n",
              "  \t  (2): RMSE()\n",
              "  \t  (3): MAPE()\n",
              "  \t  (4): MASE()\n",
              "  \t)\n",
              "  \t\"loss\":                          MASE()\n",
              "  \t\"monotone_constaints\":           {}\n",
              "  \t\"num_block_layers\":              [3, 3]\n",
              "  \t\"num_blocks\":                    [3, 3]\n",
              "  \t\"optimizer\":                     Ranger\n",
              "  \t\"optimizer_params\":              None\n",
              "  \t\"output_transformer\":            EncoderNormalizer(\n",
              "  \t\tmethod='standard',\n",
              "  \t\tcenter=True,\n",
              "  \t\tmax_length=None,\n",
              "  \t\ttransformation='relu',\n",
              "  \t\tmethod_kwargs={}\n",
              "  \t)\n",
              "  \t\"prediction_length\":             24\n",
              "  \t\"reduce_on_plateau_min_lr\":      1e-05\n",
              "  \t\"reduce_on_plateau_patience\":    1000\n",
              "  \t\"reduce_on_plateau_reduction\":   2.0\n",
              "  \t\"sharing\":                       [True, True]\n",
              "  \t\"stack_types\":                   ['trend', 'seasonality']\n",
              "  \t\"weight_decay\":                  0.01\n",
              "  \t\"widths\":                        [32, 512]\n",
              "  (loss): MASE()\n",
              "  (logging_metrics): ModuleList(\n",
              "    (0): SMAPE()\n",
              "    (1): MAE()\n",
              "    (2): RMSE()\n",
              "    (3): MAPE()\n",
              "    (4): MASE()\n",
              "  )\n",
              "  (net_blocks): ModuleList(\n",
              "    (0-2): 3 x NBEATSTrendBlock(\n",
              "      (fc): Sequential(\n",
              "        (0): Linear(in_features=60, out_features=32, bias=True)\n",
              "        (1): ReLU()\n",
              "        (2): Sequential(\n",
              "          (0): Dropout(p=0.1, inplace=False)\n",
              "          (1): Linear(in_features=32, out_features=32, bias=True)\n",
              "        )\n",
              "        (3): ReLU()\n",
              "        (4): Sequential(\n",
              "          (0): Dropout(p=0.1, inplace=False)\n",
              "          (1): Linear(in_features=32, out_features=32, bias=True)\n",
              "        )\n",
              "        (5): ReLU()\n",
              "      )\n",
              "      (theta_f_fc): Linear(in_features=32, out_features=3, bias=False)\n",
              "      (theta_b_fc): Linear(in_features=32, out_features=3, bias=False)\n",
              "    )\n",
              "    (3-5): 3 x NBEATSSeasonalBlock(\n",
              "      (fc): Sequential(\n",
              "        (0): Linear(in_features=60, out_features=512, bias=True)\n",
              "        (1): ReLU()\n",
              "        (2): Sequential(\n",
              "          (0): Dropout(p=0.1, inplace=False)\n",
              "          (1): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (3): ReLU()\n",
              "        (4): Sequential(\n",
              "          (0): Dropout(p=0.1, inplace=False)\n",
              "          (1): Linear(in_features=512, out_features=512, bias=True)\n",
              "        )\n",
              "        (5): ReLU()\n",
              "      )\n",
              "      (theta_f_fc): Linear(in_features=512, out_features=24, bias=False)\n",
              "      (theta_b_fc): Linear(in_features=512, out_features=24, bias=False)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 122,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "best_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MV39nBEj0ijF",
        "outputId": "8f0583b5-e1f5-4774-e983-ed6ceb547b0e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO: GPU available: False, used: False\n",
            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: False, used: False\n",
            "INFO: TPU available: False, using: 0 TPU cores\n",
            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO: IPU available: False, using: 0 IPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO: HPU available: False, using: 0 HPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "tensor(0.2503)"
            ]
          },
          "execution_count": 123,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "actuals = torch.cat([y[0] for x, y in iter(val_dataloader)])\n",
        "predictions = best_model.predict(val_dataloader)\n",
        "(actuals - predictions).abs().mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JBRvQ8zM0y2x",
        "outputId": "9ebbc3e0-06ff-4d8e-e127-e670ae989fe5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO: GPU available: False, used: False\n",
            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: False, used: False\n",
            "INFO: TPU available: False, using: 0 TPU cores\n",
            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO: IPU available: False, using: 0 IPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO: HPU available: False, using: 0 HPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
          ]
        }
      ],
      "source": [
        "raw_predictions, x, index, decoder_lengths, y= best_model.predict(val_dataloader, mode=\"raw\", return_x=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mlE4LXJMWdDF",
        "outputId": "f45f6e56-739e-4cf5-ade7-1ba5d803cb39"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Output(prediction=tensor([[113.4792, 113.5140, 113.5394, 113.4941, 113.4160, 113.3992, 113.5031,\n",
              "         113.6949, 113.8688, 113.9229, 113.8331, 113.6682, 113.5396, 113.5251,\n",
              "         113.6239, 113.7709, 113.8911, 113.9506, 113.9663, 113.9735, 113.9836,\n",
              "         113.9678, 113.8799, 113.6963]]), backcast=tensor([[ 90.0350,  90.3899,  90.7365,  91.0858,  91.4092,  91.6606,  91.8162,\n",
              "          91.8984,  91.9624,  92.0571,  92.1906,  92.3265,  92.4108,  92.4078,\n",
              "          92.3174,  92.1673,  91.9941,  91.8382,  91.7553,  91.8298,  92.1613,\n",
              "          92.8164,  93.7688,  94.8731,  95.9057,  96.6615,  97.0525,  97.1460,\n",
              "          97.1122,  97.1135,  97.2085,  97.3353,  97.3820,  97.2912,  97.1169,\n",
              "          96.9907,  97.0193,  97.1949,  97.3906,  97.4530,  97.3274,  97.1169,\n",
              "          97.0204,  97.1830,  97.5652,  97.9388,  98.0362,  97.7688,  97.3689,\n",
              "          97.3406,  98.2226, 100.2883, 103.3593, 106.8486, 110.0156, 112.2905,\n",
              "         113.4914, 113.8283, 113.7182, 113.5409]]), trend=tensor([[ 83.2987,  83.7768,  84.2504,  84.7196,  85.1844,  85.6448,  86.1008,\n",
              "          86.5523,  86.9995,  87.4423,  87.8807,  88.3146,  88.7442,  89.1693,\n",
              "          89.5900,  90.0064,  90.4183,  90.8258,  91.2290,  91.6277,  92.0220,\n",
              "          92.4119,  92.7974,  93.1784,  93.5551,  93.9274,  94.2953,  94.6587,\n",
              "          95.0178,  95.3724,  95.7227,  96.0685,  96.4100,  96.7470,  97.0796,\n",
              "          97.4078,  97.7316,  98.0510,  98.3660,  98.6766,  98.9828,  99.2846,\n",
              "          99.5820,  99.8749, 100.1635, 100.4477, 100.7274, 101.0028, 101.2737,\n",
              "         101.5402, 101.8024, 102.0601, 102.3134, 102.5623, 102.8068, 103.0469,\n",
              "         103.2826, 103.5139, 103.7408, 103.9632, 104.1813, 104.3950, 104.6042,\n",
              "         104.8091, 105.0095, 105.2056, 105.3972, 105.5844, 105.7672, 105.9456,\n",
              "         106.1197, 106.2893, 106.4545, 106.6152, 106.7716, 106.9236, 107.0712,\n",
              "         107.2143, 107.3531, 107.4875, 107.6174, 107.7430, 107.8641, 107.9808]]), seasonality=tensor([[103.8025, 103.6794, 103.5523, 103.4324, 103.2911, 103.0820, 102.7817,\n",
              "         102.4123, 102.0291, 101.6810, 101.3762, 101.0781, 100.7328, 100.3047,\n",
              "          99.7936,  99.2271,  98.6421,  98.0787,  97.5926,  97.2683,  97.2056,\n",
              "          97.4708,  98.0377,  98.7609,  99.4169,  99.8003,  99.8235,  99.5535,\n",
              "          99.1607,  98.8073,  98.5521,  98.3331,  98.0383,  97.6104,  97.1035,\n",
              "          96.6491,  96.3539,  96.2101,  96.0908,  95.8426,  95.4108,  94.8985,\n",
              "          94.5046,  94.3743,  94.4679,  94.5574,  94.3750,  93.8323,  93.1614,\n",
              "          92.8666,  93.4865,  95.2944,  98.1121, 101.3525, 104.2750, 106.3098,\n",
              "         107.2751, 107.3807, 107.0437, 106.6439, 106.3641, 106.1853, 106.0015,\n",
              "         105.7513, 105.4728, 105.2599, 105.1721, 105.1767, 105.1678, 105.0435,\n",
              "         104.7797, 104.4452, 104.1514, 103.9761, 103.9186, 103.9135, 103.8861,\n",
              "         103.8025, 103.6794, 103.5523, 103.4324, 103.2911, 103.0820, 102.7817]]), generic=tensor([[97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662,\n",
              "         97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662,\n",
              "         97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662,\n",
              "         97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662,\n",
              "         97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662,\n",
              "         97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662,\n",
              "         97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662,\n",
              "         97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662,\n",
              "         97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662,\n",
              "         97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662, 97.0662,\n",
              "         97.0662, 97.0662, 97.0662, 97.0662]]))"
            ]
          },
          "execution_count": 125,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "raw_predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "acTTX2KpX5Ds",
        "outputId": "a31e7ffc-69ed-4340-c33f-6f2e206d0482"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "best_model.plot_prediction(x, raw_predictions, add_loss_to_title=True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g46ZxVwDfndK"
      },
      "outputs": [],
      "source": [
        "# from pytorch_forecasting import TimeSeriesDataSet, TemporalFusionTransformer\n",
        "\n",
        "# # Load and preprocess the data\n",
        "# dataset = TimeSeriesDataSet.from_csv('data.csv', target='target', time_idx='time', group_ids=['id'])\n",
        "# dataset.prepare_training(split_into_train_val_test=[0.8, 0.1, 0.1\n",
        "# # Generate predictions\n",
        "# predictions = model.predict(dataset.test_dataloader())\n",
        "\n",
        "# # Evaluate the model\n",
        "# metric = dataset.target_normalizer.metrics['mse']\n",
        "# print(f'Test MSE: {metric(predictions, dataset.test_dataloader())}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GshK-fb_ZzRW",
        "outputId": "38bd9de3-af88-4fef-9acb-b4c8c2db0550"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO: GPU available: False, used: False\n",
            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: False, used: False\n",
            "INFO: TPU available: False, using: 0 TPU cores\n",
            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO: IPU available: False, using: 0 IPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO: HPU available: False, using: 0 HPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
          ]
        }
      ],
      "source": [
        "predictions = best_model.predict(test_dataloader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gto8RTaAoHS4",
        "outputId": "87740d59-3fd1-4f9e-d76f-1d5c8a90181e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Actual Targets: (tensor([[114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
            "         114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
            "         114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
            "         114.7212, 114.7212, 129.4048]]), None)\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "\n",
        "# Iterate through the test_dataloader to get actual values\n",
        "for batch in test_dataloader:\n",
        "    inputs, targets = batch  # Assuming your DataLoader returns inputs and targets\n",
        "    # Print or process the actual values\n",
        "    print(\"Actual Targets:\", targets)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zd1qHvQlpIox"
      },
      "outputs": [],
      "source": [
        "actuals= torch.cat([y[0] for x, y in iter(test_dataloader)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qGy-hIISpQA_"
      },
      "outputs": [],
      "source": [
        "predictions=predictions[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tuA__uOMxFrr",
        "outputId": "8eab5da0-c9cf-4c6e-ee3f-5e79b902e14b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor(0.8024)"
            ]
          },
          "execution_count": 132,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(actuals - predictions).abs().mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bJCbfCER5FYN"
      },
      "outputs": [],
      "source": [
        "from pytorch_forecasting.metrics import MAE, MASE, RMSE, SMAPE, MAPE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xOAqiyasxuwq",
        "outputId": "dbab4398-d2ae-436e-e267-9483a3ea7ef8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE: tensor(2.7272)\n",
            "MAE: tensor(0.8024)\n"
          ]
        }
      ],
      "source": [
        "# Calculate the RMSE\n",
        "rmse = RMSE()(predictions,actuals)\n",
        "\n",
        "print(\"RMSE:\", rmse)\n",
        "\n",
        "mae= MAE()(predictions,actuals)\n",
        "print(\"MAE:\", mae)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0BV0tYz1etJx",
        "outputId": "c5e77657-5c8f-44b0-dc53-84ba25a5d2d8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<torch.utils.data.dataloader.DataLoader at 0x7c0bf7d0fb80>"
            ]
          },
          "execution_count": 141,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_dataloader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "Ps9OGfYw5pHm",
        "outputId": "abc7b2b4-1f5c-4431-a067-ce47f7098f99"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        " # Plot 10 examples\n",
        "\n",
        "\n",
        "for idx in range(1):\n",
        "    best_model.plot_prediction(x, raw_predictions, idx=idx, add_loss_to_title=True)\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "Fqkjo-2w0yyN",
        "outputId": "bfb0e621-d4fc-4741-8b9a-6e66d5018d85"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for idx in range(1):  # plot 10 examples\n",
        "    best_model.plot_interpretation(x, raw_predictions, idx=idx)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c5AOlGFqsHAH",
        "outputId": "81895c23-1a9c-43ac-c84a-a46d4fb3e912"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input (x):\n",
            "(tensor([[114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
            "         114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
            "         114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
            "         114.7212, 114.7212, 129.4048]]), None)\n"
          ]
        }
      ],
      "source": [
        "import numpy\n",
        "import tensorflow as tf\n",
        "y_numpy=[]\n",
        "# Iterate through the test DataLoader and print the batches\n",
        "for batch in test_dataloader:\n",
        "    x, y = batch  # x is the input, y is the target\n",
        "    print(\"Input (x):\")\n",
        "    print(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5NogQZGAd0W",
        "outputId": "8894cc4f-e6fd-4115-e6fe-2b4340b136bb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input (x):\n",
            "{'encoder_cat': tensor([], size=(1, 60, 0), dtype=torch.int64), 'encoder_cont': tensor([[[-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.6952],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [-0.1417],\n",
            "         [ 2.4434],\n",
            "         [ 2.4434],\n",
            "         [ 2.4434],\n",
            "         [ 2.4434],\n",
            "         [ 2.4434],\n",
            "         [ 2.4434],\n",
            "         [ 2.4434],\n",
            "         [ 2.4434]]]), 'encoder_target': tensor([[ 93.6137,  93.6137,  93.6137,  93.6137,  93.6137,  93.6137,  93.6137,\n",
            "          93.6137,  93.6137,  93.6137,  93.6137,  93.6137,  93.6137,  93.6137,\n",
            "          93.6137,  93.6137,  93.6137,  93.6137,  93.6137,  93.6137,  93.6137,\n",
            "          93.6137,  97.3359,  97.3359,  97.3359,  97.3359,  97.3359,  97.3359,\n",
            "          97.3359,  97.3359,  97.3359,  97.3359,  97.3359,  97.3359,  97.3359,\n",
            "          97.3359,  97.3359,  97.3359,  97.3359,  97.3359,  97.3359,  97.3359,\n",
            "          97.3359,  97.3359,  97.3359,  97.3359,  97.3359,  97.3359,  97.3359,\n",
            "          97.3359,  97.3359,  97.3359, 114.7212, 114.7212, 114.7212, 114.7212,\n",
            "         114.7212, 114.7212, 114.7212, 114.7212]]), 'encoder_lengths': tensor([60]), 'decoder_cat': tensor([], size=(1, 24, 0), dtype=torch.int64), 'decoder_cont': tensor([[[2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [2.4434],\n",
            "         [4.6269]]]), 'decoder_target': tensor([[114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
            "         114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
            "         114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
            "         114.7212, 114.7212, 129.4048]]), 'decoder_lengths': tensor([24]), 'decoder_time_idx': tensor([[12030, 12031, 12032, 12033, 12034, 12035, 12036, 12037, 12038, 12039,\n",
            "         12040, 12041, 12042, 12043, 12044, 12045, 12046, 12047, 12048, 12049,\n",
            "         12050, 12051, 12052, 12053]]), 'groups': tensor([[0]]), 'target_scale': tensor([[98.2891,  6.7250]])}\n",
            "Target (y):\n",
            "(array([[114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
            "        114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
            "        114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
            "        114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 129.4048]],\n",
            "      dtype=float32), None)\n"
          ]
        }
      ],
      "source": [
        "import numpy\n",
        "import tensorflow as tf\n",
        "y_numpy=[]\n",
        "# Iterate through the test DataLoader and print the batches\n",
        "for batch in test_dataloader:\n",
        "    x, y = batch  # x is the input, y is the target\n",
        "    print(\"Input (x):\")\n",
        "    print(x)\n",
        "\n",
        "    with tf.compat.v1.Session() as sess:\n",
        "      y_numpy = tuple(tensor.numpy() if tensor is not None else None for tensor in y)\n",
        "      print(\"Target (y):\")\n",
        "      print(y_numpy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5nUtwhayHuxm"
      },
      "outputs": [],
      "source": [
        "for batch in test_dataloader:\n",
        "    x, y = batch  # x is the input, y is the target\n",
        "\n",
        "    # Assuming you have a trained model named 'model'\n",
        "    with torch.no_grad():\n",
        "        predictions = best_model(x)\n",
        "\n",
        "    # 'predictions' now contains the predicted values for the current batch\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u3ftH3g1LiLT",
        "outputId": "40552be0-a71b-4114-a3bc-7ad694b60ea7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
              "       114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
              "       114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
              "       114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 129.4048],\n",
              "      dtype=float32)"
            ]
          },
          "execution_count": 147,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_numpy[0][0,:24]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kvHDPWnhJH68",
        "outputId": "ed10a2d0-9038-4364-fcda-d41ad2a6dc27"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
              "       114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
              "       114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 114.7212,\n",
              "       114.7212, 114.7212, 114.7212, 114.7212, 114.7212, 129.4048],\n",
              "      dtype=float32)"
            ]
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          "execution_count": 148,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "actual_data = y_numpy[0][0,:24]\n",
        "actual_data"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SRjSmMpuLKmT",
        "outputId": "880d010b-2d0a-4d23-d6ed-c9421ece00eb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([114.626854, 114.79784 , 114.85172 , 114.738525, 114.55269 ,\n",
              "       114.439354, 114.47247 , 114.60103 , 114.69886 , 114.67213 ,\n",
              "       114.53689 , 114.40614 , 114.398285, 114.54009 , 114.742096,\n",
              "       114.86807 , 114.84726 , 114.74269 , 114.71857 , 114.923134,\n",
              "       115.36819 , 115.892784, 116.24316 , 116.220665], dtype=float32)"
            ]
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          "execution_count": 149,
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        "predictions['prediction'].numpy()[0,:24]"
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    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7i6QwpD3H2_B"
      },
      "outputs": [],
      "source": [
        "forecast_data = predictions['prediction'].numpy()[0,:24]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "257e6a98-8a05-43f9-8650-541953506e74"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([114.626854, 114.79784 , 114.85172 , 114.738525, 114.55269 ,\n",
              "       114.439354, 114.47247 , 114.60103 , 114.69886 , 114.67213 ,\n",
              "       114.53689 , 114.40614 , 114.398285, 114.54009 , 114.742096,\n",
              "       114.86807 , 114.84726 , 114.74269 , 114.71857 , 114.923134,\n",
              "       115.36819 , 115.892784, 116.24316 , 116.220665], dtype=float32)"
            ]
          },
          "execution_count": 151,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "forecast_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5HV96jtYDvxK",
        "outputId": "73fe0b62-ea56-4495-91b4-4ea3d5ccd139"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "sMAPE: 0.67\n",
            "MASE: 0.66\n",
            "MAE: 0.80\n",
            "RMSE: 2.73\n",
            "MAPE: 0.65\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "def smape(actual, forecast):\n",
        "    return 100 * np.mean(2 * np.abs(forecast - actual) / (np.abs(actual) + np.abs(forecast)))\n",
        "\n",
        "smape_value = smape(actual_data, forecast_data)\n",
        "\n",
        "def mape(actual, forecast):\n",
        "    return 100 * np.mean(np.abs((actual - forecast) / actual))\n",
        "\n",
        "mape_value = mape(actual_data, forecast_data)\n",
        "\n",
        "# Mean Absolute Scaled Error (MASE)\n",
        "def mase(actual, forecast, seasonality):\n",
        "    mae = np.mean(np.abs(actual - forecast))\n",
        "    mean_absolute_seasonal_difference = np.mean(np.abs(actual - np.roll(actual, seasonality)))\n",
        "    return mae / mean_absolute_seasonal_difference\n",
        "\n",
        "seasonality = 1  # Adjust this based on the seasonality of your data\n",
        "mase_value = mase(actual_data, forecast_data, seasonality)\n",
        "\n",
        "# Mean Absolute Error (MAE)\n",
        "mae_value = np.mean(np.abs(actual_data - forecast_data))\n",
        "\n",
        "# Root Mean Squared Error (RMSE)\n",
        "rmse_value = np.sqrt(np.mean((actual_data - forecast_data)**2))\n",
        "\n",
        "print(f'sMAPE: {smape_value:.2f}')\n",
        "print(f'MASE: {mase_value:.2f}')\n",
        "print(f'MAE: {mae_value:.2f}')\n",
        "print(f'RMSE: {rmse_value:.2f}')\n",
        "print(f'MAPE: {mape_value:.2f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pEf4_4xEoEH0",
        "outputId": "c3361fa3-7633-46bb-bd36-321f3d881f09"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training runtime: 68.27720975875854 seconds\n"
          ]
        }
      ],
      "source": [
        "# Calculate and print the runtime\n",
        "runtime = end_time - start_time\n",
        "print(f\"Training runtime: {runtime} seconds\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "du3_7jjYqHMr"
      },
      "outputs": [],
      "source": [
        "# net.predict(net.make_future_dataframe(test_data, n_historic_predictions=True))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cLGm6hWjoJqT"
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      "source": [
        "# from pytorch_forecasting.metrics import interpret_output\n",
        "\n",
        "\n",
        "# forecast_nbeats=  interpret_output(NBeats.forward(x),reduction='mean', attention_prediction_horizon=365)\n",
        "\n",
        "# forecast_nbeats"
      ]
    },
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