{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install prophet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78EFAjdvzf11",
        "outputId": "38295d3a-6f31-43cd-800b-5292024655a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: prophet in /usr/local/lib/python3.10/dist-packages (1.1.5)\n",
            "Requirement already satisfied: cmdstanpy>=1.0.4 in /usr/local/lib/python3.10/dist-packages (from prophet) (1.2.4)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.10/dist-packages (from prophet) (1.26.4)\n",
            "Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from prophet) (3.7.1)\n",
            "Requirement already satisfied: pandas>=1.0.4 in /usr/local/lib/python3.10/dist-packages (from prophet) (2.1.4)\n",
            "Requirement already satisfied: holidays>=0.25 in /usr/local/lib/python3.10/dist-packages (from prophet) (0.55)\n",
            "Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.10/dist-packages (from prophet) (4.66.5)\n",
            "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.10/dist-packages (from prophet) (6.4.4)\n",
            "Requirement already satisfied: stanio<2.0.0,>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from cmdstanpy>=1.0.4->prophet) (0.5.1)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from holidays>=0.25->prophet) (2.8.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (3.1.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.4->prophet) (2024.1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.4->prophet) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil->holidays>=0.25->prophet) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8YzlszdQytrv"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import prophet as fbp\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "# Use fivethirtyeight plot style\n",
        "plt.style.use('fivethirtyeight')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We will be using amazon share price data which can be downloaded from YAHOO finance website.\n",
        "df = pd.read_csv('AMZN.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GxqxTWiMzdDT",
        "outputId": "f911be01-ff3a-4800-c35b-b38429211135"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date       Open     High        Low      Close  Adj Close     Volume\n",
              "0  2015-01-27  15.315000  15.5120  15.131500  15.337500  15.337500   58406000\n",
              "1  2015-01-28  15.490500  15.5755  15.190000  15.195500  15.195500   61276000\n",
              "2  2015-01-29  15.236500  15.6400  14.966500  15.589000  15.589000  173132000\n",
              "3  2015-01-30  17.316000  17.9750  17.037001  17.726500  17.726500  477122000\n",
              "4  2015-02-02  17.502501  18.2500  17.500500  18.223499  18.223499  204638000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-25fe5447-6ecf-4f41-bbbb-5350fc14df62\" 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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-27</td>\n",
              "      <td>15.315000</td>\n",
              "      <td>15.5120</td>\n",
              "      <td>15.131500</td>\n",
              "      <td>15.337500</td>\n",
              "      <td>15.337500</td>\n",
              "      <td>58406000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-28</td>\n",
              "      <td>15.490500</td>\n",
              "      <td>15.5755</td>\n",
              "      <td>15.190000</td>\n",
              "      <td>15.195500</td>\n",
              "      <td>15.195500</td>\n",
              "      <td>61276000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-29</td>\n",
              "      <td>15.236500</td>\n",
              "      <td>15.6400</td>\n",
              "      <td>14.966500</td>\n",
              "      <td>15.589000</td>\n",
              "      <td>15.589000</td>\n",
              "      <td>173132000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-30</td>\n",
              "      <td>17.316000</td>\n",
              "      <td>17.9750</td>\n",
              "      <td>17.037001</td>\n",
              "      <td>17.726500</td>\n",
              "      <td>17.726500</td>\n",
              "      <td>477122000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-02-02</td>\n",
              "      <td>17.502501</td>\n",
              "      <td>18.2500</td>\n",
              "      <td>17.500500</td>\n",
              "      <td>18.223499</td>\n",
              "      <td>18.223499</td>\n",
              "      <td>204638000</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-25fe5447-6ecf-4f41-bbbb-5350fc14df62')\"\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-25fe5447-6ecf-4f41-bbbb-5350fc14df62 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-25fe5447-6ecf-4f41-bbbb-5350fc14df62');\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-9e21996d-1ba0-4ff1-92b4-e91ac80188d4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9e21996d-1ba0-4ff1-92b4-e91ac80188d4')\"\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-9e21996d-1ba0-4ff1-92b4-e91ac80188d4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2139,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 2139,\n        \"samples\": [\n          \"2015-05-07\",\n          \"2016-05-20\",\n          \"2021-04-06\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48.48953544307372,\n        \"min\": 15.2365,\n        \"max\": 187.199997,\n        \"num_unique_values\": 2058,\n        \"samples\": [\n          122.750504,\n          49.935001,\n          134.225006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.10212250979179,\n        \"min\": 15.512,\n        \"max\": 188.654007,\n        \"num_unique_values\": 2091,\n        \"samples\": [\n          96.540001,\n          18.8885,\n          34.049999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47.81326450689509,\n        \"min\": 14.9665,\n        \"max\": 184.839493,\n        \"num_unique_values\": 2098,\n        \"samples\": [\n          82.125,\n          90.892502,\n          21.121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48.43604623024123,\n        \"min\": 15.1955,\n        \"max\": 186.570496,\n        \"num_unique_values\": 2115,\n        \"samples\": [\n          181.901505,\n          161.804001,\n          24.524\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48.43604623024123,\n        \"min\": 15.1955,\n        \"max\": 186.570496,\n        \"num_unique_values\": 2115,\n        \"samples\": [\n          181.901505,\n          161.804001,\n          24.524\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40962053,\n        \"min\": 17626000,\n        \"max\": 477122000,\n        \"num_unique_values\": 2110,\n        \"samples\": [\n          60832000,\n          87775600,\n          88442000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# add two columnsin dataframe having values as Date and Adj Close\n",
        "df[['ds', 'y']] = df[['Date', 'Adj Close']]\n",
        "# Subset two columns from data frame\n",
        "df = df[['ds', 'y']]\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RoHq9p6pz9tb",
        "outputId": "fede42ba-66c7-4e4c-e709-70bd196f0a2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           ds          y\n",
              "0  2015-01-27  15.337500\n",
              "1  2015-01-28  15.195500\n",
              "2  2015-01-29  15.589000\n",
              "3  2015-01-30  17.726500\n",
              "4  2015-02-02  18.223499"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5dc946bf-035a-4af4-af0e-07bfbd8e0259\" 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>ds</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-27</td>\n",
              "      <td>15.337500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-28</td>\n",
              "      <td>15.195500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-29</td>\n",
              "      <td>15.589000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-30</td>\n",
              "      <td>17.726500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-02-02</td>\n",
              "      <td>18.223499</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-5dc946bf-035a-4af4-af0e-07bfbd8e0259')\"\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-5dc946bf-035a-4af4-af0e-07bfbd8e0259 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-5dc946bf-035a-4af4-af0e-07bfbd8e0259');\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-31f95d32-b08f-4fa3-9249-850136960ca2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-31f95d32-b08f-4fa3-9249-850136960ca2')\"\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-31f95d32-b08f-4fa3-9249-850136960ca2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2139,\n  \"fields\": [\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 2139,\n        \"samples\": [\n          \"2015-05-07\",\n          \"2016-05-20\",\n          \"2021-04-06\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48.43604623024123,\n        \"min\": 15.1955,\n        \"max\": 186.570496,\n        \"num_unique_values\": 2115,\n        \"samples\": [\n          181.901505,\n          161.804001,\n          24.524\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split data frame into two parts train and test\n",
        "split_date = \"2019-07-21\"\n",
        "df_train = df.loc[df.ds <= split_date].copy()\n",
        "df_test = df.loc[df.ds > split_date].copy()\n"
      ],
      "metadata": {
        "id": "k1emWmeS0lSc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate prophet\n",
        "model = fbp.Prophet()\n",
        "# fit the training data\n",
        "model.fit(df_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vmCkKri70mhY",
        "outputId": "fb7b29fa-b92f-4a82-b672-0dc9ebb0f263"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpup_d2oc2/hxe0u0_t.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpup_d2oc2/qqv4ebu1.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=21819', 'data', 'file=/tmp/tmpup_d2oc2/hxe0u0_t.json', 'init=/tmp/tmpup_d2oc2/qqv4ebu1.json', 'output', 'file=/tmp/tmpup_d2oc2/prophet_modelsks7t7hp/prophet_model-20240827171501.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
            "17:15:01 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "17:15:01 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<prophet.forecaster.Prophet at 0x791c01a7cb20>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast = model.predict(df_test)\n",
        "forecast.tail()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "Xc-8Mua-0nyQ",
        "outputId": "407dd279-cadb-4577-fd20-e3e265bbf52e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             ds       trend  yhat_lower  yhat_upper  trend_lower  trend_upper  \\\n",
              "1006 2023-07-20  108.893908 -116.044847  344.478995  -126.995624   339.333334   \n",
              "1007 2023-07-21  108.906862 -122.813536  346.840552  -127.470630   339.659625   \n",
              "1008 2023-07-24  108.945726 -117.822491  346.984447  -127.919726   340.011825   \n",
              "1009 2023-07-25  108.958681 -119.359019  347.150753  -128.394731   340.338116   \n",
              "1010 2023-07-26  108.971635 -118.599240  347.492601  -128.869737   340.659261   \n",
              "\n",
              "      additive_terms  additive_terms_lower  additive_terms_upper    weekly  \\\n",
              "1006        7.124716              7.124716              7.124716  1.287964   \n",
              "1007        7.006898              7.006898              7.006898  1.132721   \n",
              "1008        6.951288              6.951288              6.951288  1.053084   \n",
              "1009        7.161561              7.161561              7.161561  1.282639   \n",
              "1010        7.144089              7.144089              7.144089  1.296344   \n",
              "\n",
              "      weekly_lower  weekly_upper    yearly  yearly_lower  yearly_upper  \\\n",
              "1006      1.287964      1.287964  5.836752      5.836752      5.836752   \n",
              "1007      1.132721      1.132721  5.874177      5.874177      5.874177   \n",
              "1008      1.053084      1.053084  5.898204      5.898204      5.898204   \n",
              "1009      1.282639      1.282639  5.878922      5.878922      5.878922   \n",
              "1010      1.296344      1.296344  5.847745      5.847745      5.847745   \n",
              "\n",
              "      multiplicative_terms  multiplicative_terms_lower  \\\n",
              "1006                   0.0                         0.0   \n",
              "1007                   0.0                         0.0   \n",
              "1008                   0.0                         0.0   \n",
              "1009                   0.0                         0.0   \n",
              "1010                   0.0                         0.0   \n",
              "\n",
              "      multiplicative_terms_upper        yhat  \n",
              "1006                         0.0  116.018624  \n",
              "1007                         0.0  115.913760  \n",
              "1008                         0.0  115.897014  \n",
              "1009                         0.0  116.120241  \n",
              "1010                         0.0  116.115724  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9056ae9e-1372-4f65-862f-ec5b10563154\" 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>ds</th>\n",
              "      <th>trend</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "      <th>trend_lower</th>\n",
              "      <th>trend_upper</th>\n",
              "      <th>additive_terms</th>\n",
              "      <th>additive_terms_lower</th>\n",
              "      <th>additive_terms_upper</th>\n",
              "      <th>weekly</th>\n",
              "      <th>weekly_lower</th>\n",
              "      <th>weekly_upper</th>\n",
              "      <th>yearly</th>\n",
              "      <th>yearly_lower</th>\n",
              "      <th>yearly_upper</th>\n",
              "      <th>multiplicative_terms</th>\n",
              "      <th>multiplicative_terms_lower</th>\n",
              "      <th>multiplicative_terms_upper</th>\n",
              "      <th>yhat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1006</th>\n",
              "      <td>2023-07-20</td>\n",
              "      <td>108.893908</td>\n",
              "      <td>-116.044847</td>\n",
              "      <td>344.478995</td>\n",
              "      <td>-126.995624</td>\n",
              "      <td>339.333334</td>\n",
              "      <td>7.124716</td>\n",
              "      <td>7.124716</td>\n",
              "      <td>7.124716</td>\n",
              "      <td>1.287964</td>\n",
              "      <td>1.287964</td>\n",
              "      <td>1.287964</td>\n",
              "      <td>5.836752</td>\n",
              "      <td>5.836752</td>\n",
              "      <td>5.836752</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>116.018624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1007</th>\n",
              "      <td>2023-07-21</td>\n",
              "      <td>108.906862</td>\n",
              "      <td>-122.813536</td>\n",
              "      <td>346.840552</td>\n",
              "      <td>-127.470630</td>\n",
              "      <td>339.659625</td>\n",
              "      <td>7.006898</td>\n",
              "      <td>7.006898</td>\n",
              "      <td>7.006898</td>\n",
              "      <td>1.132721</td>\n",
              "      <td>1.132721</td>\n",
              "      <td>1.132721</td>\n",
              "      <td>5.874177</td>\n",
              "      <td>5.874177</td>\n",
              "      <td>5.874177</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>115.913760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008</th>\n",
              "      <td>2023-07-24</td>\n",
              "      <td>108.945726</td>\n",
              "      <td>-117.822491</td>\n",
              "      <td>346.984447</td>\n",
              "      <td>-127.919726</td>\n",
              "      <td>340.011825</td>\n",
              "      <td>6.951288</td>\n",
              "      <td>6.951288</td>\n",
              "      <td>6.951288</td>\n",
              "      <td>1.053084</td>\n",
              "      <td>1.053084</td>\n",
              "      <td>1.053084</td>\n",
              "      <td>5.898204</td>\n",
              "      <td>5.898204</td>\n",
              "      <td>5.898204</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>115.897014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1009</th>\n",
              "      <td>2023-07-25</td>\n",
              "      <td>108.958681</td>\n",
              "      <td>-119.359019</td>\n",
              "      <td>347.150753</td>\n",
              "      <td>-128.394731</td>\n",
              "      <td>340.338116</td>\n",
              "      <td>7.161561</td>\n",
              "      <td>7.161561</td>\n",
              "      <td>7.161561</td>\n",
              "      <td>1.282639</td>\n",
              "      <td>1.282639</td>\n",
              "      <td>1.282639</td>\n",
              "      <td>5.878922</td>\n",
              "      <td>5.878922</td>\n",
              "      <td>5.878922</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>116.120241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1010</th>\n",
              "      <td>2023-07-26</td>\n",
              "      <td>108.971635</td>\n",
              "      <td>-118.599240</td>\n",
              "      <td>347.492601</td>\n",
              "      <td>-128.869737</td>\n",
              "      <td>340.659261</td>\n",
              "      <td>7.144089</td>\n",
              "      <td>7.144089</td>\n",
              "      <td>7.144089</td>\n",
              "      <td>1.296344</td>\n",
              "      <td>1.296344</td>\n",
              "      <td>1.296344</td>\n",
              "      <td>5.847745</td>\n",
              "      <td>5.847745</td>\n",
              "      <td>5.847745</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>116.115724</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-9056ae9e-1372-4f65-862f-ec5b10563154')\"\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-9056ae9e-1372-4f65-862f-ec5b10563154 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-9056ae9e-1372-4f65-862f-ec5b10563154');\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-803781df-a956-4f59-8286-cfdf13d3d6eb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-803781df-a956-4f59-8286-cfdf13d3d6eb')\"\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-803781df-a956-4f59-8286-cfdf13d3d6eb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"forecast\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-07-20 00:00:00\",\n        \"max\": \"2023-07-26 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2023-07-21 00:00:00\",\n          \"2023-07-26 00:00:00\",\n          \"2023-07-24 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03353202490033208,\n        \"min\": 108.89390781318944,\n        \"max\": 108.97163512161303,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          108.9068623645934,\n          108.97163512161303,\n          108.94572601880517\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yhat_lower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.496295862831749,\n        \"min\": -122.81353620481549,\n        \"max\": -116.0448470177692,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -122.81353620481549,\n          -118.59923978991336,\n          -117.8224914312762\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yhat_upper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2045577707676014,\n        \"min\": 344.4789951787035,\n        \"max\": 347.49260104629553,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          346.84055180939765,\n          347.49260104629553,\n          346.98444702348513\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trend_lower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7387937965588006,\n        \"min\": -128.8697366965436,\n        \"max\": -126.99562418241307,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -127.47062958664768,\n          -128.8697366965436,\n          -127.91972588807441\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trend_upper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5266367359350765,\n        \"min\": 339.33333394778595,\n        \"max\": 340.6592608653998,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          339.6596249153613,\n          340.6592608653998,\n          340.0118249857444\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"additive_terms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09306390881978949,\n        \"min\": 6.951287742063335,\n        \"max\": 7.161560707143674,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7.006898080151015,\n          7.144088860082906,\n          6.951287742063335\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"additive_terms_lower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09306390881978949,\n        \"min\": 6.951287742063335,\n        \"max\": 7.161560707143674,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7.006898080151015,\n          7.144088860082906,\n          6.951287742063335\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"additive_terms_upper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09306390881978949,\n        \"min\": 6.951287742063335,\n        \"max\": 7.161560707143674,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7.006898080151015,\n          7.144088860082906,\n          6.951287742063335\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weekly\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11113415695996749,\n        \"min\": 1.053084105957981,\n        \"max\": 1.296344051466387,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.132720667732359,\n          1.296344051466387,\n          1.053084105957981\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weekly_lower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11113415695996749,\n        \"min\": 1.053084105957981,\n        \"max\": 1.296344051466387,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.132720667732359,\n          1.296344051466387,\n          1.053084105957981\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weekly_upper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11113415695996749,\n        \"min\": 1.053084105957981,\n        \"max\": 1.296344051466387,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.132720667732359,\n          1.296344051466387,\n          1.053084105957981\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yearly\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02476333474155894,\n        \"min\": 5.836752057349762,\n        \"max\": 5.898203636105354,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5.874177412418657,\n          5.84774480861652,\n          5.898203636105354\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yearly_lower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02476333474155894,\n        \"min\": 5.836752057349762,\n        \"max\": 5.898203636105354,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5.874177412418657,\n          5.84774480861652,\n          5.898203636105354\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yearly_upper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02476333474155894,\n        \"min\": 5.836752057349762,\n        \"max\": 5.898203636105354,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5.874177412418657,\n          5.84774480861652,\n          5.898203636105354\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"multiplicative_terms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"multiplicative_terms_lower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"multiplicative_terms_upper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yhat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10651972211572082,\n        \"min\": 115.8970137608685,\n        \"max\": 116.12024127735279,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          115.91376044474441\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.plot(forecast)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pjCHQVpH0o9R",
        "outputId": "59450f5f-d40f-4338-9824-ad2ad85fc6a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/prophet/plot.py:72: FutureWarning: The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
            "  fcst_t = fcst['ds'].dt.to_pydatetime()\n",
            "/usr/local/lib/python3.10/dist-packages/prophet/plot.py:73: FutureWarning: The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
            "  ax.plot(m.history['ds'].dt.to_pydatetime(), m.history['y'], 'k.',\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot graphs of different components:\n",
        "model.plot_components(forecast)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "u-WQQfQR0qMM",
        "outputId": "628de198-884c-4172-aa59-3956b725f139"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/prophet/plot.py:228: FutureWarning: The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
            "  fcst_t = fcst['ds'].dt.to_pydatetime()\n",
            "/usr/local/lib/python3.10/dist-packages/prophet/plot.py:351: FutureWarning: The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
            "  df_y['ds'].dt.to_pydatetime(), seas[name], ls='-', c='#0072B2')\n",
            "/usr/local/lib/python3.10/dist-packages/prophet/plot.py:354: FutureWarning: The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
            "  df_y['ds'].dt.to_pydatetime(), seas[name + '_lower'],\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# code\n",
        "print(\"Mean Squared Error (MSE):\", mean_squared_error(y_true = df_test[\"y\"], y_pred = forecast['yhat']))\n",
        "print(\"Mean Absolute Error (MAE):\", mean_absolute_error(y_true = df_test[\"y\"], y_pred = forecast['yhat']))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BMyN6RJt0rZF",
        "outputId": "94deab14-ad32-4c73-a8de-c65dfaedaf70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error (MSE): 1928.5765426797855\n",
            "Mean Absolute Error (MAE): 34.2779245809425\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def mean_abs_perc_err(y_true, y_pred):\n",
        "\treturn np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
        "\n",
        "print(\"Mean Absolute % Error (MAPE): \", mean_abs_perc_err(y_true = np.asarray(df_test[\"y\"]), y_pred = np.asarray(forecast['yhat'])))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eKgRu0090s8E",
        "outputId": "75ee7864-f22d-45ae-be4d-4cf457099cbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute % Error (MAPE):  22.615631700798787\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RfMi1pYN0uag"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}