time_series_sklearn.ipynb

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    {
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        "https://www.cienciadedatos.net/documentos/py27-time-series-forecasting-python-scikitlearn.html"
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: skforecast in /usr/local/lib/python3.8/dist-packages (0.6.0)\n",
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            "Requirement already satisfied: pyperclip>=1.6 in /usr/local/lib/python3.8/dist-packages (from cmd2>=1.0.0->cliff->optuna<3.1,>=2.10.0->skforecast) (1.8.2)\n",
            "Requirement already satisfied: attrs>=16.3.0 in /usr/local/lib/python3.8/dist-packages (from cmd2>=1.0.0->cliff->optuna<3.1,>=2.10.0->skforecast) (22.2.0)\n",
            "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from stevedore>=2.0.1->cliff->optuna<3.1,>=2.10.0->skforecast) (5.11.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.9.2 in /usr/local/lib/python3.8/dist-packages (from Mako->alembic>=1.5.0->optuna<3.1,>=2.10.0->skforecast) (2.1.2)\n"
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      "source": [
        "# Data manipulation\n",
        "# ==============================================================================\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "!pip install skforecast\n",
        "\n",
        "# Plots\n",
        "# ==============================================================================\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('fivethirtyeight')\n",
        "plt.rcParams['lines.linewidth'] = 1.5\n",
        "%matplotlib inline\n",
        "\n",
        "# Modeling and Forecasting\n",
        "# ==============================================================================\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import make_pipeline\n",
        "\n",
        "from skforecast.ForecasterAutoreg import ForecasterAutoreg\n",
        "from skforecast.ForecasterAutoregCustom import ForecasterAutoregCustom\n",
        "from skforecast.ForecasterAutoregDirect import ForecasterAutoregDirect\n",
        "from skforecast.model_selection import grid_search_forecaster\n",
        "from skforecast.model_selection import backtesting_forecaster\n",
        "from skforecast.utils import save_forecaster\n",
        "from skforecast.utils import load_forecaster\n",
        "\n",
        "# Warnings configuration\n",
        "# ==============================================================================\n",
        "import warnings\n",
        "# warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data download\n",
        "# ==============================================================================\n",
        "url = 'https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/data/h2o_exog.csv'\n",
        "data = pd.read_csv(url, sep=',')\n",
        "data"
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              "          fecha         y    exog_1    exog_2\n",
              "0    1992-04-01  0.379808  0.958792  1.166029\n",
              "1    1992-05-01  0.361801  0.951993  1.117859\n",
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              "3    1992-07-01  0.483389  0.958078  1.097376\n",
              "4    1992-08-01  0.475463  0.956370  1.122199\n",
              "..          ...       ...       ...       ...\n",
              "190  2008-02-01  0.761822  1.515840  1.786373\n",
              "191  2008-03-01  0.649435  1.506258  1.694264\n",
              "192  2008-04-01  0.827887  1.505253  1.627135\n",
              "193  2008-05-01  0.816255  1.491464  1.555068\n",
              "194  2008-06-01  0.762137  1.459856  1.463507\n",
              "\n",
              "[195 rows x 4 columns]"
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      "cell_type": "code",
      "source": [
        "# Data preparation\n",
        "# ==============================================================================\n",
        "data = data.rename(columns={'fecha': 'date'})\n",
        "data['date'] = pd.to_datetime(data['date'], format='%Y/%m/%d')\n",
        "data = data.set_index('date')\n",
        "data = data.rename(columns={'x': 'y'})\n",
        "data = data.asfreq('MS')\n",
        "data = data.sort_index()\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
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        "id": "XIhbAhz9LWmL",
        "outputId": "fff820b3-a1ac-415b-d2ab-45e1a1eabc3e"
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "                   y    exog_1    exog_2\n",
              "date                                    \n",
              "1992-04-01  0.379808  0.958792  1.166029\n",
              "1992-05-01  0.361801  0.951993  1.117859\n",
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              "      <th>1992-05-01</th>\n",
              "      <td>0.361801</td>\n",
              "      <td>0.951993</td>\n",
              "      <td>1.117859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-06-01</th>\n",
              "      <td>0.410534</td>\n",
              "      <td>0.952955</td>\n",
              "      <td>1.067942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-07-01</th>\n",
              "      <td>0.483389</td>\n",
              "      <td>0.958078</td>\n",
              "      <td>1.097376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-08-01</th>\n",
              "      <td>0.475463</td>\n",
              "      <td>0.956370</td>\n",
              "      <td>1.122199</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-303846e9-c147-4f0b-9b05-ad26e3d9bff4')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-303846e9-c147-4f0b-9b05-ad26e3d9bff4 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-303846e9-c147-4f0b-9b05-ad26e3d9bff4');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Number of rows with missing values: {data.isnull().any(axis=1).mean()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ycX1nFC8L2_M",
        "outputId": "f51e5bb2-1dee-428d-b802-16706f6543a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of rows with missing values: 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verify that a temporary index is complete\n",
        "# ==============================================================================\n",
        "(data.index == pd.date_range(start=data.index.min(),\n",
        "                             end=data.index.max(),\n",
        "                             freq=data.index.freq)).all()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8VplA2UuMLjD",
        "outputId": "086f3a74-7b76-4518-f8e8-7aef6247190f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill gaps in a temporary index\n",
        "# ==============================================================================\n",
        "# data.asfreq(freq='30min', fill_value=np.nan)"
      ],
      "metadata": {
        "id": "9wYfKwf8MRA0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split data into train-test\n",
        "# ==============================================================================\n",
        "steps = 36\n",
        "data_train = data[:-steps]\n",
        "data_test  = data[-steps:]\n",
        "\n",
        "print(f\"Train dates : {data_train.index.min()} --- {data_train.index.max()}  (n={len(data_train)})\")\n",
        "print(f\"Test dates  : {data_test.index.min()} --- {data_test.index.max()}  (n={len(data_test)})\")\n",
        "\n",
        "fig, ax=plt.subplots(figsize=(9, 4))\n",
        "data_train['y'].plot(ax=ax, label='train')\n",
        "data_test['y'].plot(ax=ax, label='test')\n",
        "ax.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "I_HXoMBoMSVl",
        "outputId": "810aa230-3632-44ee-c447-7829b98e5abb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train dates : 1992-04-01 00:00:00 --- 2005-06-01 00:00:00  (n=159)\n",
            "Test dates  : 2005-07-01 00:00:00 --- 2008-06-01 00:00:00  (n=36)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create and train forecaster\n",
        "# ==============================================================================\n",
        "forecaster = ForecasterAutoreg(\n",
        "                regressor = RandomForestRegressor(random_state=123),\n",
        "                lags      = 6\n",
        "             )\n",
        "\n",
        "forecaster.fit(y=data_train['y'])\n",
        "forecaster"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A55IIh0fNXjl",
        "outputId": "22014958-1b92-4629-8ca9-4a10dbe5f6a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "================= \n",
              "ForecasterAutoreg \n",
              "================= \n",
              "Regressor: RandomForestRegressor(random_state=123) \n",
              "Lags: [1 2 3 4 5 6] \n",
              "Transformer for y: None \n",
              "Transformer for exog: None \n",
              "Window size: 6 \n",
              "Weight function included: False \n",
              "Exogenous included: False \n",
              "Type of exogenous variable: None \n",
              "Exogenous variables names: None \n",
              "Training range: [Timestamp('1992-04-01 00:00:00'), Timestamp('2005-06-01 00:00:00')] \n",
              "Training index type: DatetimeIndex \n",
              "Training index frequency: MS \n",
              "Regressor parameters: {'bootstrap': True, 'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': None, 'max_features': 1.0, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 123, 'verbose': 0, 'warm_start': False} \n",
              "Creation date: 2023-03-05 19:18:36 \n",
              "Last fit date: 2023-03-05 19:18:36 \n",
              "Skforecast version: 0.6.0 \n",
              "Python version: 3.8.10 "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predictions\n",
        "# ==============================================================================\n",
        "# Once the model is trained, the test data is predicted (36 months into the future).\n",
        "\n",
        "steps = 36\n",
        "predictions = forecaster.predict(steps=steps)\n",
        "predictions.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5fYvrJFAOWik",
        "outputId": "37e45b13-c48d-4a2a-a8df-f7ba86094708"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2005-07-01    0.878756\n",
              "2005-08-01    0.882167\n",
              "2005-09-01    0.973184\n",
              "2005-10-01    0.983678\n",
              "2005-11-01    0.849494\n",
              "Freq: MS, Name: pred, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot\n",
        "# ==============================================================================\n",
        "fig, ax = plt.subplots(figsize=(9, 4))\n",
        "data_train['y'].plot(ax=ax, label='train')\n",
        "data_test['y'].plot(ax=ax, label='test')\n",
        "predictions.plot(ax=ax, label='predictions')\n",
        "ax.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "AvzwzYcyOtaM",
        "outputId": "d59fa9bc-5770-48be-f3ea-4d75c0c8a6ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test error\n",
        "# ==============================================================================\n",
        "error_mse = mean_squared_error(\n",
        "                y_true = data_test['y'],\n",
        "                y_pred = predictions\n",
        "            )\n",
        "\n",
        "print(f\"Test error (mse): {error_mse}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mneCdSe_O5nl",
        "outputId": "95f5ff7b-3e48-49d7-c4c9-b46262a528f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test error (mse): 0.07326833976120374\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyperparameter Grid search\n",
        "# ==============================================================================\n",
        "steps = 36\n",
        "forecaster = ForecasterAutoreg(\n",
        "                regressor = RandomForestRegressor(random_state=123),\n",
        "                lags      = 12 # This value will be replaced in the grid search\n",
        "             )\n",
        "\n",
        "# Lags used as predictors\n",
        "lags_grid = [10, 20]\n",
        "\n",
        "# Regressor's hyperparameters\n",
        "param_grid = {'n_estimators': [100, 500],\n",
        "              'max_depth': [3, 5, 10]}\n",
        "\n",
        "results_grid = grid_search_forecaster(\n",
        "                        forecaster         = forecaster,\n",
        "                        y                  = data_train['y'],\n",
        "                        param_grid         = param_grid,\n",
        "                        lags_grid          = lags_grid,\n",
        "                        steps              = steps,\n",
        "                        refit              = True,\n",
        "                        metric             = 'mean_squared_error',\n",
        "                        initial_train_size = int(len(data_train)*0.5),\n",
        "                        fixed_train_size   = False,\n",
        "                        return_best        = True,\n",
        "                        verbose            = False\n",
        "               )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nn-WHaBnPaqL",
        "outputId": "a6dd4b47-e374-4c2d-c798-9323f41a3220"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of models compared: 12.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loop lags_grid:   0%|                                               | 0/2 [00:00<?, ?it/s]\n",
            "loop param_grid:   0%|                                              | 0/6 [00:00<?, ?it/s]\u001b[A\n",
            "loop param_grid:  17%|██████▎                               | 1/6 [00:02<00:10,  2.09s/it]\u001b[A\n",
            "loop param_grid:  33%|████████████▋                         | 2/6 [00:09<00:20,  5.17s/it]\u001b[A\n",
            "loop param_grid:  50%|███████████████████                   | 3/6 [00:10<00:10,  3.40s/it]\u001b[A\n",
            "loop param_grid:  67%|█████████████████████████▎            | 4/6 [00:18<00:10,  5.10s/it]\u001b[A\n",
            "loop param_grid:  83%|███████████████████████████████▋      | 5/6 [00:20<00:03,  3.86s/it]\u001b[A\n",
            "loop param_grid: 100%|██████████████████████████████████████| 6/6 [00:26<00:00,  4.86s/it]\u001b[A\n",
            "loop lags_grid:  50%|███████████████████▌                   | 1/2 [00:26<00:26, 26.91s/it]\n",
            "loop param_grid:   0%|                                              | 0/6 [00:00<?, ?it/s]\u001b[A\n",
            "loop param_grid:  17%|██████▎                               | 1/6 [00:01<00:06,  1.31s/it]\u001b[A\n",
            "loop param_grid:  33%|████████████▋                         | 2/6 [00:09<00:21,  5.26s/it]\u001b[A\n",
            "loop param_grid:  50%|███████████████████                   | 3/6 [00:10<00:10,  3.44s/it]\u001b[A\n",
            "loop param_grid:  67%|█████████████████████████▎            | 4/6 [00:17<00:09,  4.70s/it]\u001b[A\n",
            "loop param_grid:  83%|███████████████████████████████▋      | 5/6 [00:19<00:03,  3.86s/it]\u001b[A\n",
            "loop param_grid: 100%|██████████████████████████████████████| 6/6 [00:26<00:00,  4.98s/it]\u001b[A\n",
            "loop lags_grid: 100%|███████████████████████████████████████| 2/2 [00:53<00:00, 26.86s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "`Forecaster` refitted using the best-found lags and parameters, and the whole data set: \n",
            "  Lags: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20] \n",
            "  Parameters: {'max_depth': 3, 'n_estimators': 500}\n",
            "  Backtesting metric: 0.012836389345193383\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Grid Search results\n",
        "# ==============================================================================\n",
        "results_grid"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "dMb_DDyXP72e",
        "outputId": "6b20c7f7-42d6-4ee2-be85-d7de52388a6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                 lags  \\\n",
              "7   [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
              "6   [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
              "9   [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
              "8   [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
              "11  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
              "10  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
              "0                     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
              "1                     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
              "3                     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
              "5                     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
              "2                     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
              "4                     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
              "\n",
              "                                    params  mean_squared_error  max_depth  \\\n",
              "7    {'max_depth': 3, 'n_estimators': 500}            0.012836          3   \n",
              "6    {'max_depth': 3, 'n_estimators': 100}            0.012858          3   \n",
              "9    {'max_depth': 5, 'n_estimators': 500}            0.013248          5   \n",
              "8    {'max_depth': 5, 'n_estimators': 100}            0.013364          5   \n",
              "11  {'max_depth': 10, 'n_estimators': 500}            0.013435         10   \n",
              "10  {'max_depth': 10, 'n_estimators': 100}            0.014028         10   \n",
              "0    {'max_depth': 3, 'n_estimators': 100}            0.036982          3   \n",
              "1    {'max_depth': 3, 'n_estimators': 500}            0.037345          3   \n",
              "3    {'max_depth': 5, 'n_estimators': 500}            0.037574          5   \n",
              "5   {'max_depth': 10, 'n_estimators': 500}            0.040542         10   \n",
              "2    {'max_depth': 5, 'n_estimators': 100}            0.041474          5   \n",
              "4   {'max_depth': 10, 'n_estimators': 100}            0.047870         10   \n",
              "\n",
              "    n_estimators  \n",
              "7            500  \n",
              "6            100  \n",
              "9            500  \n",
              "8            100  \n",
              "11           500  \n",
              "10           100  \n",
              "0            100  \n",
              "1            500  \n",
              "3            500  \n",
              "5            500  \n",
              "2            100  \n",
              "4            100  "
            ],
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              "      <th></th>\n",
              "      <th>lags</th>\n",
              "      <th>params</th>\n",
              "      <th>mean_squared_error</th>\n",
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              "      <th>7</th>\n",
              "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>{'max_depth': 5, 'n_estimators': 500}</td>\n",
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              "      <td>10</td>\n",
              "      <td>500</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
              "      <td>{'max_depth': 10, 'n_estimators': 100}</td>\n",
              "      <td>0.014028</td>\n",
              "      <td>10</td>\n",
              "      <td>100</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>{'max_depth': 3, 'n_estimators': 100}</td>\n",
              "      <td>0.036982</td>\n",
              "      <td>3</td>\n",
              "      <td>100</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>{'max_depth': 3, 'n_estimators': 500}</td>\n",
              "      <td>0.037345</td>\n",
              "      <td>3</td>\n",
              "      <td>500</td>\n",
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              "      <th>3</th>\n",
              "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
              "      <td>{'max_depth': 5, 'n_estimators': 500}</td>\n",
              "      <td>0.037574</td>\n",
              "      <td>5</td>\n",
              "      <td>500</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
              "      <td>{'max_depth': 10, 'n_estimators': 500}</td>\n",
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              "      <td>10</td>\n",
              "      <td>500</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d03806f4-a10f-4e89-854c-1d7eab704231')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create and train forecaster with the best hyperparameters\n",
        "# ==============================================================================\n",
        "regressor = RandomForestRegressor(max_depth=3, n_estimators=500, random_state=123)\n",
        "forecaster = ForecasterAutoreg(\n",
        "                regressor = regressor,\n",
        "                lags      = 20\n",
        "             )\n",
        "\n",
        "forecaster.fit(y=data_train['y'])"
      ],
      "metadata": {
        "id": "YXPypbBoQHcN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predictions\n",
        "# ==============================================================================\n",
        "predictions = forecaster.predict(steps=steps)"
      ],
      "metadata": {
        "id": "hgbSv87oQQu7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot\n",
        "# ==============================================================================\n",
        "fig, ax = plt.subplots(figsize=(9, 4))\n",
        "data_train['y'].plot(ax=ax, label='train')\n",
        "data_test['y'].plot(ax=ax, label='test')\n",
        "predictions.plot(ax=ax, label='predictions')\n",
        "ax.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "xhTg-c1KQUW8",
        "outputId": "29593f2b-667d-4f11-a80d-8c43673d6d86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test error\n",
        "# ==============================================================================\n",
        "error_mse = mean_squared_error(\n",
        "                y_true = data_test['y'],\n",
        "                y_pred = predictions\n",
        "            )\n",
        "\n",
        "print(f\"Test error (mse): {error_mse}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZRvo0NVQYdz",
        "outputId": "7be7e8bf-99fd-495f-b4c1-571a4f088aa4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test error (mse): 0.004392699665157793\n"
          ]
        }
      ]
    }
  ]
}