Forecasting_Automation.ipynb

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      "name": "Forecasting_Automation.ipynb",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/pruthamodak/Stock_Price_Prediction_using_Facebook_Prophet/blob/main/Forecasting_Automation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mrG0EKYUXRoY"
      },
      "source": [
        "# Forecasting Automation for Stock Price prediction using Facebook Prophet "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EJPImZ5za2DY"
      },
      "source": [
        "## Importing all the necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNPlTwMfbBbv"
      },
      "source": [
        "#Necessary libraries = Pandas, fbprophet and plotly\n",
        "\n",
        "#pandas= data Manipulation and analysis\n",
        "#fbprophet = Forecasting\n",
        "#plotly= data visualization\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tR4fFCxE8fAg"
      },
      "source": [
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "from fbprophet import Prophet"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yK5iokzObG8b"
      },
      "source": [
        " #Initializing Plotly\n",
        "import plotly.io as pio\n",
        "pio.renderers.default='colab'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQr_B55bbjQ1"
      },
      "source": [
        "## Importing the Dataset & Exploring it"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Here, we are using AMAZON stocks data\n",
        "## Download it from Yahoo Finance -> Historical Data\n",
        "## Similary, we can predict stocks for any company."
      ],
      "metadata": {
        "id": "wx_Jp1qiFV-b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "76a6siA7bma0"
      },
      "source": [
        "df = pd.read_csv('AMZN.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vezMkspZboYX"
      },
      "source": [
        "#read_csv function from pandas"
      ],
      "execution_count": null,
      "outputs": []
    },
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        "df"
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        {
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          "data": {
            "text/plain": [
              "           Date         Open         High          Low        Close  \\\n",
              "0    2021-03-24  3151.040039  3160.310059  3085.149902  3087.070068   \n",
              "1    2021-03-25  3072.989990  3109.780029  3037.139893  3046.260010   \n",
              "2    2021-03-26  3044.060059  3056.659912  2996.000000  3052.030029   \n",
              "3    2021-03-29  3055.439941  3091.250000  3028.449951  3075.729980   \n",
              "4    2021-03-30  3070.010010  3073.000000  3034.000000  3055.290039   \n",
              "..          ...          ...          ...          ...          ...   \n",
              "249  2022-03-18  3136.260010  3231.879883  3120.219971  3225.010010   \n",
              "250  2022-03-21  3222.419922  3261.679932  3191.060059  3229.830078   \n",
              "251  2022-03-22  3236.110107  3323.340088  3233.979980  3297.780029   \n",
              "252  2022-03-23  3274.100098  3327.399902  3253.739990  3268.159912   \n",
              "253  2022-03-24  3274.989990  3282.370117  3201.000000  3272.989990   \n",
              "\n",
              "       Adj Close   Volume  \n",
              "0    3087.070068  2959000  \n",
              "1    3046.260010  3563500  \n",
              "2    3052.030029  3312900  \n",
              "3    3075.729980  2746000  \n",
              "4    3055.290039  2337600  \n",
              "..           ...      ...  \n",
              "249  3225.010010  5141300  \n",
              "250  3229.830078  3326900  \n",
              "251  3297.780029  3204300  \n",
              "252  3268.159912  2790600  \n",
              "253  3272.989990  2594990  \n",
              "\n",
              "[254 rows x 7 columns]"
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
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              "      <th>Volume</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-03-24</td>\n",
              "      <td>3151.040039</td>\n",
              "      <td>3160.310059</td>\n",
              "      <td>3085.149902</td>\n",
              "      <td>3087.070068</td>\n",
              "      <td>3087.070068</td>\n",
              "      <td>2959000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-03-25</td>\n",
              "      <td>3072.989990</td>\n",
              "      <td>3109.780029</td>\n",
              "      <td>3037.139893</td>\n",
              "      <td>3046.260010</td>\n",
              "      <td>3046.260010</td>\n",
              "      <td>3563500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-03-26</td>\n",
              "      <td>3044.060059</td>\n",
              "      <td>3056.659912</td>\n",
              "      <td>2996.000000</td>\n",
              "      <td>3052.030029</td>\n",
              "      <td>3052.030029</td>\n",
              "      <td>3312900</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-03-29</td>\n",
              "      <td>3055.439941</td>\n",
              "      <td>3091.250000</td>\n",
              "      <td>3028.449951</td>\n",
              "      <td>3075.729980</td>\n",
              "      <td>3075.729980</td>\n",
              "      <td>2746000</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-03-30</td>\n",
              "      <td>3070.010010</td>\n",
              "      <td>3073.000000</td>\n",
              "      <td>3034.000000</td>\n",
              "      <td>3055.290039</td>\n",
              "      <td>3055.290039</td>\n",
              "      <td>2337600</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>2022-03-18</td>\n",
              "      <td>3136.260010</td>\n",
              "      <td>3231.879883</td>\n",
              "      <td>3120.219971</td>\n",
              "      <td>3225.010010</td>\n",
              "      <td>3225.010010</td>\n",
              "      <td>5141300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>2022-03-21</td>\n",
              "      <td>3222.419922</td>\n",
              "      <td>3261.679932</td>\n",
              "      <td>3191.060059</td>\n",
              "      <td>3229.830078</td>\n",
              "      <td>3229.830078</td>\n",
              "      <td>3326900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>2022-03-22</td>\n",
              "      <td>3236.110107</td>\n",
              "      <td>3323.340088</td>\n",
              "      <td>3233.979980</td>\n",
              "      <td>3297.780029</td>\n",
              "      <td>3297.780029</td>\n",
              "      <td>3204300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>2022-03-23</td>\n",
              "      <td>3274.100098</td>\n",
              "      <td>3327.399902</td>\n",
              "      <td>3253.739990</td>\n",
              "      <td>3268.159912</td>\n",
              "      <td>3268.159912</td>\n",
              "      <td>2790600</td>\n",
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              "    <tr>\n",
              "      <th>253</th>\n",
              "      <td>2022-03-24</td>\n",
              "      <td>3274.989990</td>\n",
              "      <td>3282.370117</td>\n",
              "      <td>3201.000000</td>\n",
              "      <td>3272.989990</td>\n",
              "      <td>3272.989990</td>\n",
              "      <td>2594990</td>\n",
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              "<p>254 rows × 7 columns</p>\n",
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      "source": [
        "## Close Price column ---- Standard over the entire finance industry\n",
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 254 entries, 0 to 253\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Date       254 non-null    object \n",
            " 1   Open       254 non-null    float64\n",
            " 2   High       254 non-null    float64\n",
            " 3   Low        254 non-null    float64\n",
            " 4   Close      254 non-null    float64\n",
            " 5   Adj Close  254 non-null    float64\n",
            " 6   Volume     254 non-null    int64  \n",
            "dtypes: float64(5), int64(1), object(1)\n",
            "memory usage: 14.0+ KB\n"
          ]
        }
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      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "              Open         High          Low        Close    Adj Close  \\\n",
              "count   254.000000   254.000000   254.000000   254.000000   254.000000   \n",
              "mean   3319.296699  3355.487831  3280.856064  3317.448472  3317.448472   \n",
              "std     199.042844   195.026348   205.178665   200.719458   200.719458   \n",
              "min    2733.669922  2805.000000  2671.449951  2720.290039  2720.290039   \n",
              "25%    3212.382507  3248.737488  3196.260010  3223.842529  3223.842529   \n",
              "50%    3342.165039  3368.664917  3303.854981  3335.119995  3335.119995   \n",
              "75%    3451.909973  3483.157532  3412.632507  3447.789917  3447.789917   \n",
              "max    3744.000000  3773.080078  3696.790039  3731.409912  3731.409912   \n",
              "\n",
              "             Volume  \n",
              "count  2.540000e+02  \n",
              "mean   3.454030e+06  \n",
              "std    1.413073e+06  \n",
              "min    1.680300e+06  \n",
              "25%    2.570175e+06  \n",
              "50%    3.182750e+06  \n",
              "75%    3.842425e+06  \n",
              "max    1.267280e+07  "
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              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
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              "      <th>count</th>\n",
              "      <td>254.000000</td>\n",
              "      <td>254.000000</td>\n",
              "      <td>254.000000</td>\n",
              "      <td>254.000000</td>\n",
              "      <td>254.000000</td>\n",
              "      <td>2.540000e+02</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3319.296699</td>\n",
              "      <td>3355.487831</td>\n",
              "      <td>3280.856064</td>\n",
              "      <td>3317.448472</td>\n",
              "      <td>3317.448472</td>\n",
              "      <td>3.454030e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>199.042844</td>\n",
              "      <td>195.026348</td>\n",
              "      <td>205.178665</td>\n",
              "      <td>200.719458</td>\n",
              "      <td>200.719458</td>\n",
              "      <td>1.413073e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2733.669922</td>\n",
              "      <td>2805.000000</td>\n",
              "      <td>2671.449951</td>\n",
              "      <td>2720.290039</td>\n",
              "      <td>2720.290039</td>\n",
              "      <td>1.680300e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3212.382507</td>\n",
              "      <td>3248.737488</td>\n",
              "      <td>3196.260010</td>\n",
              "      <td>3223.842529</td>\n",
              "      <td>3223.842529</td>\n",
              "      <td>2.570175e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3342.165039</td>\n",
              "      <td>3368.664917</td>\n",
              "      <td>3303.854981</td>\n",
              "      <td>3335.119995</td>\n",
              "      <td>3335.119995</td>\n",
              "      <td>3.182750e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3451.909973</td>\n",
              "      <td>3483.157532</td>\n",
              "      <td>3412.632507</td>\n",
              "      <td>3447.789917</td>\n",
              "      <td>3447.789917</td>\n",
              "      <td>3.842425e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3744.000000</td>\n",
              "      <td>3773.080078</td>\n",
              "      <td>3696.790039</td>\n",
              "      <td>3731.409912</td>\n",
              "      <td>3731.409912</td>\n",
              "      <td>1.267280e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62b5db65-f8ac-4ad5-b657-705c5618271b')\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\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",
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              "\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "          document.querySelector('#df-62b5db65-f8ac-4ad5-b657-705c5618271b 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-62b5db65-f8ac-4ad5-b657-705c5618271b');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v3X8Z1fFb6or"
      },
      "source": [
        "# Data Visualization using plotly express- Visualizing the historical performance of Tesla"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PKVDQq2ih4S"
      },
      "source": [
        "#Line graph, Area graph , box plot (Analyzing price and volume)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sBuNquI5Qpe3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "1151f65f-bfdb-4928-bc8d-ee6075e6ad8d"
      },
      "source": [
        "px.area(df, x='Date', y='Close')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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zLP/ABZ3jlfTkcfoAeNUV0HjRWXxTdb9COiOdpeyByqnHJXgDB68cc1q6DBBq/w28RWPkxm906SPUYX2jeY/uSDPXAGDj1NAHFVpaHY2Go37Q6jqS6fAIncTNHvywHC4z3r0fxJoNinwxt9OtbVBq2jJa3N46oN7faN2VPc4LL+lX7GysIPiHb+HhaWzppWhPFOfLUiSfy97sfU5I+mKAPLofD9zN4VuvEAkjFrBcrbFCTvZ2GfyAqfXfDg0XRtCvxdGY6pbtOU2bfLwcYzk5/Sux0jxBe2nwSvjGlqfJ1FbZd9sjfIyZOcjluep5FWdc8TzeHPA/gw2NnZtey2T/wCk3ECytGgYfKobIGSeTjsKAPJ6K7v4ix211ZeG9cjtILW51OxMlytumxGdWxuwOhOab8J7azu/GTxX9uk9t9inLxuM5AXt6H3oA4aivWPCXiWHxPDr1lrOiaXJp1hpsuoW9vDbCPyjEVIUMvzYOcHnJ/GqlprLeMPAPigapY6er6bFDNZvbWyxGHLkFRtHTAxQBwmpWNhaWGnTWmpLdT3ERe4hEe37O2eFJzzWbXpt7b6JBH8OZdVgiSwkgLXbBMbxvHLY5I9fbNdDqdr4jMN9IdF0TxJ4adHEMelrEpgU52shVdwYe26gDxGrWm29vdana293dC1tpZVSW4K7vKUnBbHfA5r0jwNGJ/B3l+G7nR4PE32pjONQVDJJFj5RFvBGPX/9VMvra9b4j+FItc8MQ6ZetcxJcvGFMN7+8HzBVG3ODg4J6jpQB5zqEEFtqV1b2tyLm3imdIpwu3zUBIDY7ZHOPeq1es6aNPsL/wCJl3PplpdJZXJaCGaMFFYTuFGB/DnbkDqBjpVK31dvGngDxK2rWVj9q0tIZ7S4gt1iZAXwy/KORgdPf6YAPM6K9L1jVW+H+kaDpuj2dkLu8sI769up7dJWlMmcJ8wOFGCOP8arfEWWxvPDnhHUbLTrax+120zyx26BV37wGx7ZzgdgcUAee1veEfDg8Uay9g10bYLbyT7wm/OwZxjI61p/DnTLG91jUL7Ubdbq20nTptQNu/3ZWQDCn25z+Fdd8P8Axrc6/wCIrq11KysC5sp2tpYLZYmgwpyoK9VIzwc8gUAeQUV6F4Hj1dvD903hzw5Fcan9oHmardiMxwxgfcXzPlDZ5J54P0rX8ZwXEfgTS9e1GPSH1211YQNPYiN1ZdhcLIE+UkFRx6fU0AeeeIPD914duLSC7kjeS5tI7oCMkhVfOAc98CsmvVviPq0l54j8OWF3ZLd2jWlpcvb28KpLKxyCisBkA5wAOPSug0yLxQ/iS2t9R0XQNM8PXE4jXTblbYN5THAAA+cvj8z2oA8Jrf8ACnhn/hI7q7M14tnYWNs11d3BQuURfRR1J9KqeJbOHTvFWsWNsu2C2vZoY1znCq5AH5Cuy+HiJ4Z0668YatcbNIk32H2NYxI18WGSmCQAB1yfQigDC8ReFrLT9Dste0fUnvtLupWgzLD5UkUijO0jJByOciuWr17xl4a1DXrWy/4Rz7HH4PtrF721ZMoiYx5gkJyTJn17Z7g15DQB2k/w8ul+HFp4vtbkzrIWM9sI8GJA5XcDn5hkDPAxn2rJ8J+HB4mvr22N0bf7NZS3e4Ju3bAPl6jGc9a72LxNJ4W8HfD67KedZyx38N5bnkTQtKoYY9e4+lWNE8MR+HvGeqzWD+do2oaFdXFhOOQ0ZCnbn1XOPy9aAPHKK9C06WHwh8OLPX7Wztp9Y1S6kjiuLiISC3ij4O1TxuJ7+ho1KWHxh8ObzX7qztoNY0u6jjkuLeIRi4jk4G5RxuB7+goA89ora8IRpN410GKVFeN9Rt1ZWGQwMi5BHcV6GfFYt/ig3hmDRtKXRZdU+xy27WiMZN0m1nLEZzkkjsBgYoA8ior1zw34TtLTVvGd9b2tlcS6Tdm206G/cCFGMjAM5Y4O0AYz1Pvipr3TLzWPC+sjxU+gfa7a2a4sLuzmgWUOvJiITGQw45H9KAPHaK9mtdD1Dw/4R0I+HI9Fjvb+1W8u7y/kh81t4yqKJOigeg5P41BqvhLT9b8QeEjOunW99qErRapb6dMjRnZ829Qp+UsoOcd6APLtF04avr2naYZfKF5dRW/mbd2zewXOO+M0a1pw0jXtR0wS+aLO6lt/M27d+xiucds4r0fTPHXm/ESy0o6TpqaKmopBb26Wqq0JEgCSBwN24HBPPPIxzXDeM/8AkevEP/YTuf8A0a1AENv4furjwxe6+JI1tbWdICpJ3szc8D0ArJr0/QvEF7Z/BbUzElqfs9/HAm+2Rsqwyc5HJ5OCeRUWqaifh/4f8P2uj2lmNQ1GxS/u72eBZXIf7qLuBAAwaAPNaK7rxlBZ6r4S0PxZBZQWV3ePLb3kVuu2N3Q8OF7EjOf85h+FcFpceO7dL6BJ7b7POzo67gQI2P50AcXRXp3hXxMvi7xLH4b1TS9PGjXweKCCC2RGtDtJVkcDdkYGSSe9XfC/hqfSPBk+q6dFpcutzXz20c+oOgSCOPIJQPwWLD8v1APJKK9T8X6W154Gk1PWToya9aXCgSafNFm5hbAO5UONwJ646VZ8deKj4V1SLTNH0nSoxNZQzXEktosjO5UYIzwAABxjqSe9AHJ6z4T0rQfDVlc32sS/2zfWkd5BZR2+UEbnjc+euMn8Me9cfXtPiV5/FHifwV4euhbraahplpc3BSBFfPzlgrAZUYBwBwPSuem8feR4pfS00XSv+EeS5Nt9g+yJzGG27t2N2/HOc9aAPN6K9i0/RdL8K638QIWsYL2DTYI57aK4XeASd6A98AlQfUCqfhrxAmv2nirWtZ0nTbiSz06IxxLbhEZldipIHXnGfUDFAHlNFes6b4kXWfAGta3q+kaXdXujTQmyP2VUVTIdmGVcBlHXB4yBmszWr8+KPhSNbv7W0XUrTVRarPbwLEXjMe7awUYPJ/T65APOaK2vCOjx6/4t0zSpmKw3E6rIR129Wx74Br1qaHV4dfayjsfCi+GUm8o2Ly2+TEDjcSTu34569aAPC6K9J06PRtG+IWq+E5oo77Q9SlFtFJGBK8JfBjdGGTlSwBx1x7YpnjGKDwDZ2fhayhim1AOl5fX8sAPmHOURNw+4Mc+p49RQBx+veH7rw9JYR3ckbveWcd4ojJIVXJwD74H61k17L4tM/inxf4O8PXXkLaXllaXErRwqr5IfcAwGQMZwvQHtV+eHV4NfayisfCi+GY5vKNg8tvkxA4LEk7t+OevWgDwuivXNH0zRtA1Hx+qW1pqdlYW6S2qy4kQ5O5RkdQCQDzzg1n2Wtt4z8FeJo9XsrEz6dbpc2c9vbLE0XzYK/KOhH9fbAB5nWloNjYalqqW2p6kunWrKxa4aPeFIBIGMjqeK9Q8Pw3dz4Q0lfA0uiG+WNv7StbpIzcyy5/6aA5THTkf4V/D9olx8Zo4r/wANRaSz2sjS2LAPGXETZdRjABIyMZHvQB5LRXb2kEGu/Cq8SOGMajoV0Ji6qA8lvJwQT1OG59hWwugWht/BXg+SNEutRkGoahIFAkCP9xM9R8gPHrg0AeYUV7pPDq0GvtZQ2PhRPDMc3kmxeW33NEDgsWJ3b8c9eteTeMNMs9G8W6lYafMs1nFLmF0feNhAYDPfGcfhQBiVvL4cDeAm8TfajuXUvsP2fZ/0zD7t2ffGMfjXR6RNH4S+HEXiG2tbabV9RvWt4p7iISC3iQc7QeNxPf0q5rOu/wBv/Bg3ElpbW90uuKLg28YjWZvJPzlRwCRgHHpnvQB5lWl4f0S48R67aaRaPGk9y+1WkJCjAJJOPYGvT7b/AISi3sLCTQdA0zRNGECbn1T7OGuDj5ndn+Yg+wHFXraOHQvj3Z2um21pBBfwo8qRRqUUmMsTHx8oJHbHWgDxOaMwzyRE5KMVz64NMr0Lw/f32s+LNVuYvDkOr6t5RS1VYUSC3IOPMdcBfTk45rprvT9W1LwR4iXxXFosl5Z2wuLU2ph8+Eg858rop6UAeL0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFbVlq1vb+FdU0x1kM91JE0ZAG0BTk5OaxaKqMnHVGVajGqlGXRp/NO6Cumg1TRtQ8PWWm6sbuGWxZzFLbqrB1Y5IIJ6/4VzNFEZuJNfDxrJXbTTumt07Nfk2jqtR8Q6ZOnh+KzguIotMlYur4JK71IIOeSQpJ6cmmHxDZ/a/EkuybbqSMsPyjjLZ+bnj9a5iirdaV7/1tY5o5ZQjHl1+/vLm/M7HwFeLbNqf2qLzLGKBbqTPRXjYFPxJrlLu5kvbya6mOZJnLsfcnNXJNe1OXR49Ja6P2JOkQUDPOeSBk8+tZtE53ioroVh8K4V6leaScrba6LrstX19Fubkmr20fhCLSLVZBPLcGa6dgADjhVHPI6H6ioPDmr/2HrtvesGaJSVlVerIeD/j+FZVFT7SV1LsafVKTpzpNXU73+f8AWh1Nrq2iG11PSbhbqPTri48+3ljUb4yOgIzyMcVHf6vpC+F30bTY7r/j6WbzZgMyfKQScHjsAPauaoqvbStYyWXUlPmu907X0utL/h6HU3niCxltdGvLf7THqumxRQhGUGJlTvnrzTpr7wrdakdWlj1BJXfzZLJVUoz5ycNn7pPtmuUoo9tLqhLLaSXuya32fRu9vTt1XRnTR+KEnuteurxHEuo2xijWMZCngAHJ6AAVS0zVbez8Pa1YSLIZr0QiIqBtGxiTnn3rGope1le/r+JosBRUXFKyvF/+A2t+SNm31W3i8J3elsshuJrlJVOBtwBznmtXV9W8Pa7dpqd4+oRz+WqyWsaqVJA6KxPA/CuRopqq7W6EywFNz9om1K7d0+9k/wAl5nW3nimym8WaXrMNvKsVvEiSQ8ZUjIIU55GDxnFRjWNHs59a+xm8eK/tWRPNRcrIxyc4PSuWop+3luQsroJKKvZJLfondfczpIdU0rUtFtNP1g3MMtluEFxAobKE52sC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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ASIJnLjScv9B"
      },
      "source": [
        "## Data Preperation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8VU3NvfDczau",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "9a412cfe-0f37-4af0-aaa0-dc31443dba64"
      },
      "source": [
        "### create new df with Date and Close Price\n",
        "columns = ['Date','Close']\n",
        "ndf = pd.DataFrame(df, columns=columns)\n",
        "ndf"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date        Close\n",
              "0    2021-03-24  3087.070068\n",
              "1    2021-03-25  3046.260010\n",
              "2    2021-03-26  3052.030029\n",
              "3    2021-03-29  3075.729980\n",
              "4    2021-03-30  3055.290039\n",
              "..          ...          ...\n",
              "249  2022-03-18  3225.010010\n",
              "250  2022-03-21  3229.830078\n",
              "251  2022-03-22  3297.780029\n",
              "252  2022-03-23  3268.159912\n",
              "253  2022-03-24  3272.989990\n",
              "\n",
              "[254 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-08dfe63a-f787-4eda-b22a-5c00b7b6bfd5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .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>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-03-24</td>\n",
              "      <td>3087.070068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-03-25</td>\n",
              "      <td>3046.260010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-03-26</td>\n",
              "      <td>3052.030029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-03-29</td>\n",
              "      <td>3075.729980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-03-30</td>\n",
              "      <td>3055.290039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>2022-03-18</td>\n",
              "      <td>3225.010010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>2022-03-21</td>\n",
              "      <td>3229.830078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>2022-03-22</td>\n",
              "      <td>3297.780029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>2022-03-23</td>\n",
              "      <td>3268.159912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253</th>\n",
              "      <td>2022-03-24</td>\n",
              "      <td>3272.989990</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>254 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08dfe63a-f787-4eda-b22a-5c00b7b6bfd5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sO8g9RCI95Xe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "bcd8d6a6-fdc5-4018-cdd1-629af07e7606"
      },
      "source": [
        "### Facebook Prophet doesn't understand Date and Close\n",
        "### We need to rename the columns to ds and y respectively\n",
        "prophet_df = ndf.rename(columns={'Date':'ds', 'Close':'y'})\n",
        "prophet_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             ds            y\n",
              "0    2021-03-24  3087.070068\n",
              "1    2021-03-25  3046.260010\n",
              "2    2021-03-26  3052.030029\n",
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              "4    2021-03-30  3055.290039\n",
              "..          ...          ...\n",
              "249  2022-03-18  3225.010010\n",
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              "                                                     [key], {});\n",
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              "\n",
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j57-ly4Jc5va"
      },
      "source": [
        "## Creating Facebook Prophet Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GdFXAfTFc7iw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0bc1a92-bad9-41a8-dfa9-06949540f44f"
      },
      "source": [
        "m = Prophet()\n",
        "m.fit(prophet_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<fbprophet.forecaster.Prophet at 0x7f3eb89efa90>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T-0eY9xOdA4S"
      },
      "source": [
        "##Forecasting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGPpV0kmdCjh"
      },
      "source": [
        "future = m.make_future_dataframe(periods=30) # future dataframe with prophet period 30 Days\n",
        "forecast = m.predict(future) # make prediction from future dataframe"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JzPozfvMDUUX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "outputId": "4327c82c-aa73-4bed-ced8-f983bbc169e4"
      },
      "source": [
        "forecast"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            ds        trend   yhat_lower   yhat_upper  trend_lower  \\\n",
              "0   2021-03-24  3190.487126  3034.872285  3390.768797  3190.487126   \n",
              "1   2021-03-25  3192.632018  3052.241187  3384.818466  3192.632018   \n",
              "2   2021-03-26  3194.776909  3035.387483  3388.684628  3194.776909   \n",
              "3   2021-03-29  3201.211585  3037.309683  3383.053714  3201.211585   \n",
              "4   2021-03-30  3203.356477  3051.850196  3410.556140  3203.356477   \n",
              "..         ...          ...          ...          ...          ...   \n",
              "279 2022-04-19  2809.986100  2657.026689  3006.777767  2802.563560   \n",
              "280 2022-04-20  2805.857176  2650.761977  3004.153754  2798.095255   \n",
              "281 2022-04-21  2801.728252  2660.943692  2996.273227  2793.403812   \n",
              "282 2022-04-22  2797.599329  2644.252205  3003.172587  2788.508529   \n",
              "283 2022-04-23  2793.470405  2555.460747  2920.316623  2783.671589   \n",
              "\n",
              "     trend_upper  additive_terms  additive_terms_lower  additive_terms_upper  \\\n",
              "0    3190.487126       24.372998             24.372998             24.372998   \n",
              "1    3192.632018       23.731278             23.731278             23.731278   \n",
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              "3    3201.211585       15.795904             15.795904             15.795904   \n",
              "4    3203.356477       24.877911             24.877911             24.877911   \n",
              "..           ...             ...                   ...                   ...   \n",
              "279  2818.417511       24.877911             24.877911             24.877911   \n",
              "280  2814.884944       24.372998             24.372998             24.372998   \n",
              "281  2811.334753       23.731278             23.731278             23.731278   \n",
              "282  2807.586251       17.417362             17.417362             17.417362   \n",
              "283  2803.849895      -53.097726            -53.097726            -53.097726   \n",
              "\n",
              "        weekly  weekly_lower  weekly_upper  multiplicative_terms  \\\n",
              "0    24.372998     24.372998     24.372998                   0.0   \n",
              "1    23.731278     23.731278     23.731278                   0.0   \n",
              "2    17.417362     17.417362     17.417362                   0.0   \n",
              "3    15.795904     15.795904     15.795904                   0.0   \n",
              "4    24.877911     24.877911     24.877911                   0.0   \n",
              "..         ...           ...           ...                   ...   \n",
              "279  24.877911     24.877911     24.877911                   0.0   \n",
              "280  24.372998     24.372998     24.372998                   0.0   \n",
              "281  23.731278     23.731278     23.731278                   0.0   \n",
              "282  17.417362     17.417362     17.417362                   0.0   \n",
              "283 -53.097726    -53.097726    -53.097726                   0.0   \n",
              "\n",
              "     multiplicative_terms_lower  multiplicative_terms_upper         yhat  \n",
              "0                           0.0                         0.0  3214.860123  \n",
              "1                           0.0                         0.0  3216.363295  \n",
              "2                           0.0                         0.0  3212.194271  \n",
              "3                           0.0                         0.0  3217.007489  \n",
              "4                           0.0                         0.0  3228.234388  \n",
              "..                          ...                         ...          ...  \n",
              "279                         0.0                         0.0  2834.864011  \n",
              "280                         0.0                         0.0  2830.230174  \n",
              "281                         0.0                         0.0  2825.459530  \n",
              "282                         0.0                         0.0  2815.016691  \n",
              "283                         0.0                         0.0  2740.372679  \n",
              "\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3216.363295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-03-26</td>\n",
              "      <td>3194.776909</td>\n",
              "      <td>3035.387483</td>\n",
              "      <td>3388.684628</td>\n",
              "      <td>3194.776909</td>\n",
              "      <td>3194.776909</td>\n",
              "      <td>17.417362</td>\n",
              "      <td>17.417362</td>\n",
              "      <td>17.417362</td>\n",
              "      <td>17.417362</td>\n",
              "      <td>17.417362</td>\n",
              "      <td>17.417362</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3212.194271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-03-29</td>\n",
              "      <td>3201.211585</td>\n",
              "      <td>3037.309683</td>\n",
              "      <td>3383.053714</td>\n",
              "      <td>3201.211585</td>\n",
              "      <td>3201.211585</td>\n",
              "      <td>15.795904</td>\n",
              "      <td>15.795904</td>\n",
              "      <td>15.795904</td>\n",
              "      <td>15.795904</td>\n",
              "      <td>15.795904</td>\n",
              "      <td>15.795904</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3217.007489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-03-30</td>\n",
              "      <td>3203.356477</td>\n",
              "      <td>3051.850196</td>\n",
              "      <td>3410.556140</td>\n",
              "      <td>3203.356477</td>\n",
              "      <td>3203.356477</td>\n",
              "      <td>24.877911</td>\n",
              "      <td>24.877911</td>\n",
              "      <td>24.877911</td>\n",
              "      <td>24.877911</td>\n",
              "      <td>24.877911</td>\n",
              "      <td>24.877911</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3228.234388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>2022-04-19</td>\n",
              "      <td>2809.986100</td>\n",
              "      <td>2657.026689</td>\n",
              "      <td>3006.777767</td>\n",
              "      <td>2802.563560</td>\n",
              "      <td>2818.417511</td>\n",
              "      <td>24.877911</td>\n",
              "      <td>24.877911</td>\n",
              "      <td>24.877911</td>\n",
              "      <td>24.877911</td>\n",
              "      <td>24.877911</td>\n",
              "      <td>24.877911</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2834.864011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>2022-04-20</td>\n",
              "      <td>2805.857176</td>\n",
              "      <td>2650.761977</td>\n",
              "      <td>3004.153754</td>\n",
              "      <td>2798.095255</td>\n",
              "      <td>2814.884944</td>\n",
              "      <td>24.372998</td>\n",
              "      <td>24.372998</td>\n",
              "      <td>24.372998</td>\n",
              "      <td>24.372998</td>\n",
              "      <td>24.372998</td>\n",
              "      <td>24.372998</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2830.230174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281</th>\n",
              "      <td>2022-04-21</td>\n",
              "      <td>2801.728252</td>\n",
              "      <td>2660.943692</td>\n",
              "      <td>2996.273227</td>\n",
              "      <td>2793.403812</td>\n",
              "      <td>2811.334753</td>\n",
              "      <td>23.731278</td>\n",
              "      <td>23.731278</td>\n",
              "      <td>23.731278</td>\n",
              "      <td>23.731278</td>\n",
              "      <td>23.731278</td>\n",
              "      <td>23.731278</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2825.459530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>282</th>\n",
              "      <td>2022-04-22</td>\n",
              "      <td>2797.599329</td>\n",
              "      <td>2644.252205</td>\n",
              "      <td>3003.172587</td>\n",
              "      <td>2788.508529</td>\n",
              "      <td>2807.586251</td>\n",
              "      <td>17.417362</td>\n",
              "      <td>17.417362</td>\n",
              "      <td>17.417362</td>\n",
              "      <td>17.417362</td>\n",
              "      <td>17.417362</td>\n",
              "      <td>17.417362</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2815.016691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>283</th>\n",
              "      <td>2022-04-23</td>\n",
              "      <td>2793.470405</td>\n",
              "      <td>2555.460747</td>\n",
              "      <td>2920.316623</td>\n",
              "      <td>2783.671589</td>\n",
              "      <td>2803.849895</td>\n",
              "      <td>-53.097726</td>\n",
              "      <td>-53.097726</td>\n",
              "      <td>-53.097726</td>\n",
              "      <td>-53.097726</td>\n",
              "      <td>-53.097726</td>\n",
              "      <td>-53.097726</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2740.372679</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>284 rows × 16 columns</p>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19-Qc5qDdH8K"
      },
      "source": [
        "## yhat column is the most important --- predicted value"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "px.line(forecast, x='ds', y='yhat')"
      ],
      "metadata": {
        "id": "he9viFjNwi9T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "117a1696-ad17-471e-84fe-28ea66975f94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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    {
      "cell_type": "code",
      "source": [
        "figure = m.plot(forecast, xlabel='ds', ylabel='y')"
      ],
      "metadata": {
        "id": "XuerdTWRwjTJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "b5fcd002-611f-44ff-924b-db758dcab500"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "figure2 = m.plot_components(forecast)"
      ],
      "metadata": {
        "id": "LUqivJ0fxLJR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "1514ad3f-b3fa-4f6c-8bc7-a735aaedb330"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6csAGFe7DTij"
      },
      "source": [
        "##Downloading the Forecast data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFchZPzWDY9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "b84fdc92-d547-4295-9dc0-ea3aac3221af"
      },
      "source": [
        "from google.colab import files\n",
        "forecast.to_csv('forecast.csv')\n",
        "files.download('forecast.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_645bdaea-9086-4fec-8cd1-ce305465f6c9\", \"forecast.csv\", 71080)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%shell\n",
        "jupyter nbconvert --to html /content/Forecasting_Automation.ipynb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PuNRHN7SHaCC",
        "outputId": "24419b73-93a4-42bb-a1ab-18634cf9d4f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NbConvertApp] Converting notebook /content/Forecasting_Automation.ipynb to html\n",
            "[NbConvertApp] Writing 597347 bytes to /content/Forecasting_Automation.html\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "3GESmBO9Hpof"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}