Prophet.ipynb

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      "source": [
        "!pip install autogluon.timeseries[all] seaborn[stats] prophet"
      ],
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        "id": "qkm4yOczd6eK"
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "from pandas import DataFrame\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from datetime import timedelta\n",
        "import calendar\n",
        "from prophet import Prophet\n"
      ],
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        "id": "_ibes5L-GXfn"
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      "execution_count": null,
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      "source": [
        "df = pd.read_csv(\"drive/MyDrive/Colab Data/AutoGluon/PJM_Load_hourly.csv\")\n",
        "df.head(10)"
      ],
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              "         Datetime  PJM_Load_MW\n",
              "0   4/1/1998 1:00        22259\n",
              "1   4/1/1998 2:00        21244\n",
              "2   4/1/1998 3:00        20651\n",
              "3   4/1/1998 4:00        20421\n",
              "4   4/1/1998 5:00        20713\n",
              "5   4/1/1998 6:00        22526\n",
              "6   4/1/1998 7:00        25772\n",
              "7   4/1/1998 8:00        28724\n",
              "8   4/1/1998 9:00        30075\n",
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      "source": [
        "#prep data in required prophet format\n",
        "df['ds'] = pd.to_datetime(df['Datetime'])\n",
        "df['y'] = df['PJM_Load_MW']\n",
        "\n",
        "df = df[['ds', 'y']]\n",
        "df.head(10)\n"
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              "                   ds      y\n",
              "0 1998-04-01 01:00:00  22259\n",
              "1 1998-04-01 02:00:00  21244\n",
              "2 1998-04-01 03:00:00  20651\n",
              "3 1998-04-01 04:00:00  20421\n",
              "4 1998-04-01 05:00:00  20713\n",
              "5 1998-04-01 06:00:00  22526\n",
              "6 1998-04-01 07:00:00  25772\n",
              "7 1998-04-01 08:00:00  28724\n",
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      "source": [
        "#instantiate prophet and build the model\n",
        "m = Prophet(changepoint_prior_scale=0.05)\n",
        "model = m.fit(df)"
      ],
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        {
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          "text": [
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmplh9ikfvi/l0wdnowy.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmplh9ikfvi/dkgl1jba.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=74288', 'data', 'file=/tmp/tmplh9ikfvi/l0wdnowy.json', 'init=/tmp/tmplh9ikfvi/dkgl1jba.json', 'output', 'file=/tmp/tmplh9ikfvi/prophet_modelgfo0xc0r/prophet_model-20230622005147.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
            "00:51:48 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "00:52:41 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
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    {
      "cell_type": "code",
      "source": [
        "test_data = pd.read_csv(\"drive/MyDrive/Colab Data/AutoGluon/test_energy.csv\")\n",
        "test = pd.DataFrame()\n",
        "test['ds'] = test_data['timestamp']\n",
        "test['y'] = test_data['target']\n",
        "test.head(10)"
      ],
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              "                ds      y\n",
              "0  12/30/2001 0:00  30080\n",
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              "      <td>30080</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12/30/2001 1:00</td>\n",
              "      <td>28326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12/30/2001 2:00</td>\n",
              "      <td>27362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12/30/2001 3:00</td>\n",
              "      <td>27108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12/30/2001 4:00</td>\n",
              "      <td>26865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>12/30/2001 5:00</td>\n",
              "      <td>26808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>12/30/2001 6:00</td>\n",
              "      <td>27352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>12/30/2001 7:00</td>\n",
              "      <td>28403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>12/30/2001 8:00</td>\n",
              "      <td>29305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>12/30/2001 9:00</td>\n",
              "      <td>30329</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "          const element = document.querySelector('#df-0fd7850a-490d-43f8-8c6a-0f5a399de3ff');\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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              "        }\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "future = model.make_future_dataframe(periods=len(test), freq='H')\n",
        "forecast = model.predict(future)"
      ],
      "metadata": {
        "id": "ZWcsZviK09Ts"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "forecast[['ds', 'trend', 'weekly', 'yearly', 'yhat']].tail()"
      ],
      "metadata": {
        "id": "OGDYS-WU1HLe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2a2913ee-6c48-4328-ab50-197f16137ba5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       ds         trend       weekly       yearly  \\\n",
              "32891 2001-12-31 20:00:00  29263.913565  1363.027626  1193.271792   \n",
              "32892 2001-12-31 21:00:00  29263.674463  1412.642039  1193.213146   \n",
              "32893 2001-12-31 22:00:00  29263.435361  1453.866283  1193.157975   \n",
              "32894 2001-12-31 23:00:00  29263.196259  1487.081009  1193.106383   \n",
              "32895 2002-01-01 00:00:00  29262.957157  1512.720010  1193.058474   \n",
              "\n",
              "               yhat  \n",
              "32891  35822.854793  \n",
              "32892  35297.348906  \n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "hours_to_forecast=49\n",
        "r2_score = metrics.r2_score(list(test['y']), list(forecast.loc[len(forecast)-hours_to_forecast:,'yhat']))\n",
        "mae = metrics.mean_absolute_error(list(test['y']), list(forecast.loc[len(forecast)-hours_to_forecast:,'yhat']))\n",
        "mse = metrics.mean_squared_error(list(test['y']), list(forecast.loc[len(forecast)-hours_to_forecast:,'yhat']))\n",
        "\n",
        "print(f'r2_score : {r2_score}')\n",
        "print(f'mae : {mae}')\n",
        "print(f'mse : {mse}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KnVroy36Aafm",
        "outputId": "8603d8d0-0c5a-4b17-e29e-89a21745e2c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "r2_score : -0.3393820504792866\n",
            "mae : 2972.2425067305667\n",
            "mse : 12503110.764199948\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from autogluon.timeseries import TimeSeriesDataFrame\n",
        "\n",
        "if not 'item_id' in df:\n",
        "  df.insert(0, \"item_id\", \"H1\")\n",
        "\n",
        "plot_data = TimeSeriesDataFrame.from_data_frame(df,timestamp_column='ds')\n",
        "plot_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "fEHGpCM5PXNt",
        "outputId": "78d39fd2-9ade-4065-ac2b-abe1e19db82b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: '/usr/local/lib/python3.10/dist-packages/torchvision/image.so: undefined symbol: _ZN3c104cuda20CUDACachingAllocator9allocatorE'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?\n",
            "  warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 y\n",
              "item_id timestamp                 \n",
              "H1      1998-04-01 01:00:00  22259\n",
              "        1998-04-01 02:00:00  21244\n",
              "        1998-04-01 03:00:00  20651\n",
              "        1998-04-01 04:00:00  20421\n",
              "        1998-04-01 05:00:00  20713"
            ],
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              "      <th>1998-04-01 01:00:00</th>\n",
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              "    }\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-e11cefd8-5e40-4539-bf0c-f22f4ed830ea 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-e11cefd8-5e40-4539-bf0c-f22f4ed830ea');\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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fix_plot_data = plot_data.to_regular_index(freq='H')\n",
        "print(f\"Data has frequency '{fix_plot_data.freq}'\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QK1K9UCTQT6o",
        "outputId": "49ef116f-a7f5-406a-c909-5cbb38012cfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data has frequency 'H'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Does it have any missing data? '{fix_plot_data.isnull().values.any()}'\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MHVaRk-iQWmd",
        "outputId": "907e49ce-96d0-470d-a7da-5abac07e491b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Does it have any missing data? 'True'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if fix_plot_data.isnull().values.any():\n",
        "  fix_plot_data = fix_plot_data.fill_missing_values()\n",
        "\n",
        "print(f\"Does it have any missing data? '{fix_plot_data.isnull().values.any()}'\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUWUxGdTQYW4",
        "outputId": "b81c4399-201a-4341-af1c-f68d8b52a162"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Does it have any missing data? 'False'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import seaborn as sb\n",
        "# import matplotlib.pyplot as plt\n",
        "# from autogluon.timeseries import TimeSeriesDataFrame\n",
        "\n",
        "# sb.set_style(\"darkgrid\")\n",
        "# # TimeSeriesDataFrame can also be loaded directly from a file\n",
        "# test_data = TimeSeriesDataFrame.from_path(\"drive/MyDrive/Colab Data/AutoGluon/test_energy.csv\")\n",
        "\n",
        "\n",
        "\n",
        "# plt.figure(figsize=(20, 3))\n",
        "\n",
        "# item_id = \"H1\"\n",
        "# y_past = fix_plot_data.loc[item_id][\"y\"]\n",
        "# y_pred = forecast[\"yhat\"]\n",
        "# y_test = test_data.loc[\"H1\"][\"target\"][-48:]\n",
        "\n",
        "# plt.plot(y_past, label=\"Past time series values\")\n",
        "# plt.plot(y_pred, label=\"Predicted forecast\")\n",
        "# plt.plot(y_test, label=\"Future time series values\")\n",
        "\n",
        "# # plt.fill_between(\n",
        "# #     y_pred.index, y_pred[\"0.1\"], y_pred[\"0.9\"], color=\"red\", alpha=0.1, label=f\"10%-90% confidence interval\"\n",
        "# # )\n",
        "# plt.legend();"
      ],
      "metadata": {
        "id": "nu9bnaojQcLE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sb\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sb.set_style(\"darkgrid\")\n",
        "\n",
        "# Convert 'ds' to datetime in forecast dataframe\n",
        "forecast['ds'] = pd.to_datetime(forecast['ds'])\n",
        "\n",
        "# Convert 'ds' to datetime in historical dataframe\n",
        "df['ds'] = pd.to_datetime(df['ds'])\n",
        "\n",
        "# Convert 'ds' to datetime in test dataframe\n",
        "test['ds'] = pd.to_datetime(test['ds'])\n",
        "\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(20, 3))\n",
        "\n",
        "# Get the last 200 data points of historical data\n",
        "start_time_hist = df['ds'].iloc[-200]\n",
        "\n",
        "# Get the corresponding data from the historical dataframe\n",
        "df_limit = df[df['ds'] >= start_time_hist]\n",
        "\n",
        "# Plot the last 200 data points from the historical data\n",
        "plt.plot(df_limit['ds'], df_limit['y'], label=\"Past time series values\")\n",
        "\n",
        "# Get the start time of the forecasted data\n",
        "start_time_forecast = df['ds'].max()\n",
        "\n",
        "# Get the end time of the forecasted data (48 hours after the start)\n",
        "end_time_forecast = start_time_forecast + pd.Timedelta(hours=48)\n",
        "\n",
        "# Get the corresponding data from the forecast dataframe\n",
        "forecast_limit = forecast[(forecast['ds'] >= start_time_forecast) & (forecast['ds'] <= end_time_forecast)]\n",
        "\n",
        "# Plot the forecasted data\n",
        "plt.plot(forecast_limit['ds'], forecast_limit['yhat'], label=\"Mean forecast\")\n",
        "\n",
        "# Get the corresponding data from the test dataframe\n",
        "test_limit = test[(test['ds'] >= start_time_forecast) & (test['ds'] <= end_time_forecast)]\n",
        "\n",
        "# Plot the test data\n",
        "plt.plot(test_limit['ds'], test_limit['y'], label=\"Future time series values\")\n",
        "\n",
        "# Add the legend\n",
        "plt.legend();\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "bzYC-VQnOYvU",
        "outputId": "52f691af-a9c3-4a68-ddf7-67b82abb3cc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hardcore Model Tuning (only needs to be run once to get the params)"
      ],
      "metadata": {
        "id": "nOKzsDGQEDa5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# #this is 'Merica so add a couple of holidays where energy usage might be higher\n",
        "\n",
        "# black_friday = pd.DataFrame({\n",
        "#   'holiday': 'black_friday',\n",
        "#   'ds': pd.to_datetime(['1999-11-26','2000-11-24', '2001-11-23', '2002-11-29']),\n",
        "#   'lower_window': -10,\n",
        "#   'upper_window': 1,\n",
        "# })\n",
        "# xmas = pd.DataFrame({\n",
        "#   'holiday': 'xmas',\n",
        "#   'ds': pd.to_datetime(['1999-12-24', '1999-12-25', '1999-12-26','2000-12-24', '2000-12-25', '2000-12-26', '2001-12-24', '2001-12-25', '2001-12-26']),\n",
        "#   'lower_window': -5,\n",
        "#   'upper_window': 1,\n",
        "# })\n",
        "\n",
        "# tg = pd.DataFrame({\n",
        "#   'holiday': 'xmas',\n",
        "#   'ds': pd.to_datetime(['1999-11-25', '2000-11-23', '2001-12-22','2002-12-28']),\n",
        "#   'lower_window': -5,\n",
        "#   'upper_window': 1,\n",
        "# })\n",
        "\n",
        "# holidays = pd.concat((black_friday, xmas))"
      ],
      "metadata": {
        "id": "UzV0sK7WETQa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Create the grid\n",
        "# changepoint_prior_scale = [0.01, 0.03, 0.05, 0.07] # default 0.05\n",
        "# seasonality_prior_scale = [1, 5, 10, 15] # default 10\n",
        "# holidays_prior_scale = [1, 5, 10, 15] # default 10\n",
        "# seasonality_mode = ['additive']\n",
        "# changepoint_range = [0.6, 0.7, 0.8, 0.9] # default 0.8\n",
        "\n",
        "# # Compute the total number of iterations\n",
        "# total_iter = len(changepoint_prior_scale)*len(seasonality_prior_scale)*len(holidays_prior_scale)*len(seasonality_mode)*len(changepoint_range)\n",
        "# print(f'Number of iterations : {total_iter}')\n",
        "\n",
        "# # Loop over the parameters, build and assess the models\n",
        "# grid_search_results = []\n",
        "# iteration = 1\n",
        "# for cps in changepoint_prior_scale:\n",
        "#     for sps in seasonality_prior_scale:\n",
        "#         for hps in holidays_prior_scale:\n",
        "#             for sm in seasonality_mode:\n",
        "#                 for cr in changepoint_range:\n",
        "#                     m = Prophet(holidays=holidays,\n",
        "#                                  changepoint_prior_scale = cps,\n",
        "#                                  seasonality_prior_scale = sps,\n",
        "#                                  holidays_prior_scale = hps,\n",
        "#                                  seasonality_mode = sm,\n",
        "#                                  changepoint_range = cr)\n",
        "#                     m.add_country_holidays(country_name='US')\n",
        "#                     model = m.fit(df)\n",
        "#                     future = model.make_future_dataframe(periods=len(test), freq='H')\n",
        "#                     forecast = model.predict(future)\n",
        "\n",
        "#                     r2_score = metrics.r2_score(list(test['y']), list(forecast.loc[len(forecast)-hours_to_forecast:,'yhat']))\n",
        "#                     mae = metrics.mean_absolute_error(list(test['y']), list(forecast.loc[len(forecast)-hours_to_forecast:,'yhat']))\n",
        "#                     mse = metrics.mean_squared_error(list(test['y']), list(forecast.loc[len(forecast)-hours_to_forecast:,'yhat']))\n",
        "\n",
        "#                     print(f'iteration : {iteration} / {total_iter} ')\n",
        "#                     print(f'r2_score : {r2_score}')\n",
        "#                     print(f'mae : {mae}')\n",
        "#                     print(f'mse : {mse}')\n",
        "\n",
        "#                     grid_search_results.append([iteration, cps, sps, hps, sm, cr, r2_score, mae, mse])\n",
        "#                     iteration += 1\n",
        "\n",
        "# # Store the results in a dataframe\n",
        "# grid_search_df = pd.DataFrame(grid_search_results, columns = ['iteration', 'cps', 'sps', 'hps', 'sm', 'cr', 'r2_score', 'mae', 'mse'])"
      ],
      "metadata": {
        "id": "1jjZCQBpEDI9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# grid_search_df.sort_values('mse', ascending = True).head()"
      ],
      "metadata": {
        "id": "2F9R98otGtyC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# replace these with the output of the hyperparameter tuning above\n",
        "changepoint_prior_scale = 0.05 # default 0.05\n",
        "seasonality_prior_scale = 10 # default 10\n",
        "holidays_prior_scale = 5 # default 10\n",
        "seasonality_mode = 'additive'\n",
        "changepoint_range = 0.6 # default 0.8\n",
        "\n",
        "# Creating an instance of the Prophet class and training the model\n",
        "m3 = Prophet(holidays=holidays,\n",
        "            changepoint_prior_scale = changepoint_prior_scale,\n",
        "            seasonality_prior_scale = seasonality_prior_scale,\n",
        "            holidays_prior_scale = holidays_prior_scale,\n",
        "            seasonality_mode = seasonality_mode,\n",
        "            changepoint_range = changepoint_range)\n",
        "m3.add_country_holidays(country_name='US')\n",
        "model3 = m3.fit(df)\n",
        "\n",
        "future3 = model3.make_future_dataframe(periods=len(test), freq='H')\n",
        "forecast3 = model3.predict(future3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w9x7eke0WKmK",
        "outputId": "cb2464cf-8a30-4f69-9cc4-b34fab93e555"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmplh9ikfvi/jw5joygm.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmplh9ikfvi/gq04vr46.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=68952', 'data', 'file=/tmp/tmplh9ikfvi/jw5joygm.json', 'init=/tmp/tmplh9ikfvi/gq04vr46.json', 'output', 'file=/tmp/tmplh9ikfvi/prophet_modelozfyj2jp/prophet_model-20230622010020.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
            "01:00:20 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "01:01:21 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#then re-plot\n",
        "import seaborn as sb\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sb.set_style(\"darkgrid\")\n",
        "\n",
        "#use new data\n",
        "forecast = forecast3\n",
        "\n",
        "# Convert 'ds' to datetime in forecast dataframe\n",
        "forecast['ds'] = pd.to_datetime(forecast['ds'])\n",
        "\n",
        "# Convert 'ds' to datetime in historical dataframe\n",
        "df['ds'] = pd.to_datetime(df['ds'])\n",
        "\n",
        "# Convert 'ds' to datetime in test dataframe\n",
        "test['ds'] = pd.to_datetime(test['ds'])\n",
        "\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(20, 3))\n",
        "\n",
        "# Get the last 200 data points of historical data\n",
        "start_time_hist = df['ds'].iloc[-200]\n",
        "\n",
        "# Get the corresponding data from the historical dataframe\n",
        "df_limit = df[df['ds'] >= start_time_hist]\n",
        "\n",
        "# Plot the last 200 data points from the historical data\n",
        "plt.plot(df_limit['ds'], df_limit['y'], label=\"Past time series values\")\n",
        "\n",
        "# Get the start time of the forecasted data\n",
        "start_time_forecast = df['ds'].max()\n",
        "\n",
        "# Get the end time of the forecasted data (48 hours after the start)\n",
        "end_time_forecast = start_time_forecast + pd.Timedelta(hours=48)\n",
        "\n",
        "# Get the corresponding data from the forecast dataframe\n",
        "forecast_limit = forecast[(forecast['ds'] >= start_time_forecast) & (forecast['ds'] <= end_time_forecast)]\n",
        "\n",
        "# Plot the forecasted data\n",
        "plt.plot(forecast_limit['ds'], forecast_limit['yhat'], label=\"Mean forecast\")\n",
        "\n",
        "# Get the corresponding data from the test dataframe\n",
        "test_limit = test[(test['ds'] >= start_time_forecast) & (test['ds'] <= end_time_forecast)]\n",
        "\n",
        "# Plot the test data\n",
        "plt.plot(test_limit['ds'], test_limit['y'], label=\"Future time series values\")\n",
        "\n",
        "# Add the legend\n",
        "plt.legend();\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "wAur6KsrWUvG",
        "outputId": "b86b6656-ab00-4094-f2dc-adc749828a40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x300 with 1 Axes>"
            ],
            "image/png": 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}