Prophet_Model_With_dash.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/SergJohn/Crypto-Prediction-2022-Project/blob/main/Prophet_Model_With_dash.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0qgJeVOZgkxh"
      },
      "outputs": [],
      "source": [
        "#pip install --upgrade pandas pip install --upgrade pandas-datareader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CgKDuXcUgrYP"
      },
      "outputs": [],
      "source": [
        "#pip install pandas-datareader\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oEDn47BXg2X0"
      },
      "outputs": [],
      "source": [
        "#!pip install pystan fbprophet\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1F7Q0zseg-Bz"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from fbprophet import Prophet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ruk4urW-hZKj"
      },
      "outputs": [],
      "source": [
        "import pandas_datareader as pdr\n",
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "from matplotlib import pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ChmeovZoUIJW"
      },
      "outputs": [],
      "source": [
        "# Data1 Using static Dataset from Yahoo Finance, data from 2014 afterwards\n",
        "#data_btc = pd.read_csv('BTC-USD.csv',index_col = [0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EXeCeBe-hf7k"
      },
      "outputs": [],
      "source": [
        "# Data2 Getting Yahoo Finance from pdr = data from 2020 afterwards\n",
        "data_btc = pdr.get_data_yahoo('BTC-USD')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
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        "id": "KShTpC9WhiqI",
        "outputId": "d61289f7-9b7c-4379-e4a0-a680af05609a"
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      "outputs": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
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              "    <tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-05-03</th>\n",
              "      <td>1492.770020</td>\n",
              "      <td>1447.489990</td>\n",
              "      <td>1453.780029</td>\n",
              "      <td>1490.089966</td>\n",
              "      <td>583795968</td>\n",
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              "      <th>2017-05-04</th>\n",
              "      <td>1608.910034</td>\n",
              "      <td>1490.719971</td>\n",
              "      <td>1490.719971</td>\n",
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              "      <td>933548992</td>\n",
              "      <td>1537.670044</td>\n",
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              "    <tr>\n",
              "      <th>2017-05-05</th>\n",
              "      <td>1618.030029</td>\n",
              "      <td>1530.310059</td>\n",
              "      <td>1540.869995</td>\n",
              "      <td>1555.449951</td>\n",
              "      <td>946035968</td>\n",
              "      <td>1555.449951</td>\n",
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              "    <tr>\n",
              "      <th>2017-05-06</th>\n",
              "      <td>1578.800049</td>\n",
              "      <td>1542.500000</td>\n",
              "      <td>1556.810059</td>\n",
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              "      <td>582529984</td>\n",
              "      <td>1578.800049</td>\n",
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              "    <tr>\n",
              "      <th>2017-05-07</th>\n",
              "      <td>1596.719971</td>\n",
              "      <td>1559.760010</td>\n",
              "      <td>1579.469971</td>\n",
              "      <td>1596.709961</td>\n",
              "      <td>1080029952</td>\n",
              "      <td>1596.709961</td>\n",
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            "text/plain": [
              "                   High          Low         Open        Close      Volume  \\\n",
              "Date                                                                         \n",
              "2017-05-03  1492.770020  1447.489990  1453.780029  1490.089966   583795968   \n",
              "2017-05-04  1608.910034  1490.719971  1490.719971  1537.670044   933548992   \n",
              "2017-05-05  1618.030029  1530.310059  1540.869995  1555.449951   946035968   \n",
              "2017-05-06  1578.800049  1542.500000  1556.810059  1578.800049   582529984   \n",
              "2017-05-07  1596.719971  1559.760010  1579.469971  1596.709961  1080029952   \n",
              "\n",
              "              Adj Close  \n",
              "Date                     \n",
              "2017-05-03  1490.089966  \n",
              "2017-05-04  1537.670044  \n",
              "2017-05-05  1555.449951  \n",
              "2017-05-06  1578.800049  \n",
              "2017-05-07  1596.709961  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_btc.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "<bound method DataFrame.info of                     High           Low          Open         Close  \\\n",
              "Date                                                                 \n",
              "2017-05-03   1492.770020   1447.489990   1453.780029   1490.089966   \n",
              "2017-05-04   1608.910034   1490.719971   1490.719971   1537.670044   \n",
              "2017-05-05   1618.030029   1530.310059   1540.869995   1555.449951   \n",
              "2017-05-06   1578.800049   1542.500000   1556.810059   1578.800049   \n",
              "2017-05-07   1596.719971   1559.760010   1579.469971   1596.709961   \n",
              "...                  ...           ...           ...           ...   \n",
              "2022-04-28  40269.464844  38941.421875  39241.429688  39773.828125   \n",
              "2022-04-29  39887.269531  38235.535156  39768.617188  38609.824219   \n",
              "2022-04-30  38771.210938  37697.941406  38605.859375  37714.875000   \n",
              "2022-05-01  38627.859375  37585.789062  37713.265625  38469.093750   \n",
              "2022-05-02  39061.562500  38453.156250  38491.511719  38793.300781   \n",
              "\n",
              "                 Volume     Adj Close  \n",
              "Date                                   \n",
              "2017-05-03    583795968   1490.089966  \n",
              "2017-05-04    933548992   1537.670044  \n",
              "2017-05-05    946035968   1555.449951  \n",
              "2017-05-06    582529984   1578.800049  \n",
              "2017-05-07   1080029952   1596.709961  \n",
              "...                 ...           ...  \n",
              "2022-04-28  33903704907  39773.828125  \n",
              "2022-04-29  30882994649  38609.824219  \n",
              "2022-04-30  23895713731  37714.875000  \n",
              "2022-05-01  27002760110  38469.093750  \n",
              "2022-05-02  28852674560  38793.300781  \n",
              "\n",
              "[1826 rows x 6 columns]>"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_btc.info"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kwuZ_xmjrg9z"
      },
      "outputs": [],
      "source": [
        "# For Data2 - Transforming the index in a Date columns\n",
        "data_btc=data_btc.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HN_bYr2Srn69",
        "outputId": "d245fc4f-430e-44ca-96bc-4510efd620b1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1826 entries, 0 to 1825\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype         \n",
            "---  ------     --------------  -----         \n",
            " 0   Date       1826 non-null   datetime64[ns]\n",
            " 1   High       1826 non-null   float64       \n",
            " 2   Low        1826 non-null   float64       \n",
            " 3   Open       1826 non-null   float64       \n",
            " 4   Close      1826 non-null   float64       \n",
            " 5   Volume     1826 non-null   int64         \n",
            " 6   Adj Close  1826 non-null   float64       \n",
            "dtypes: datetime64[ns](1), float64(5), int64(1)\n",
            "memory usage: 100.0 KB\n"
          ]
        }
      ],
      "source": [
        "data_btc.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pszoy6Q2VBfE"
      },
      "outputs": [],
      "source": [
        "# For Data2\n",
        "data_btc.drop(['High', 'Low', 'Open', 'Volume', 'Adj Close'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pE2RWG-Pzwyy"
      },
      "outputs": [],
      "source": [
        ""
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        "id": "NwZ9Nvrcmrf5",
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              "      <th>1821</th>\n",
              "      <td>2022-04-28</td>\n",
              "      <td>39773.828125</td>\n",
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              "      <td>37714.875000</td>\n",
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              "    <tr>\n",
              "      <th>1824</th>\n",
              "      <td>2022-05-01</td>\n",
              "      <td>38469.093750</td>\n",
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              "      <th>1825</th>\n",
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            "text/plain": [
              "           Date         Close\n",
              "1821 2022-04-28  39773.828125\n",
              "1822 2022-04-29  38609.824219\n",
              "1823 2022-04-30  37714.875000\n",
              "1824 2022-05-01  38469.093750\n",
              "1825 2022-05-02  38793.300781"
            ]
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          "execution_count": 13,
          "metadata": {},
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      ],
      "source": [
        "data_btc.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CKAgLXo8mgX3"
      },
      "outputs": [],
      "source": [
        "data_btc.columns = ['ds','y']"
      ]
    },
    {
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      "execution_count": null,
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        "id": "BcjfzLWLxsmp",
        "outputId": "6c238124-8bbb-4869-d841-5bfa52af1181"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x1c58161b4f0>"
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          "execution_count": 15,
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\n",
            "text/plain": [
              "<Figure size 396x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize = (5.5, 5.5))\n",
        "ax = fig.add_subplot(2, 1, 1)\n",
        "fig.subplots_adjust(hspace = 0.5)\n",
        "data_btc['y'].plot(ax = ax)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3YKh0qxzssIb",
        "outputId": "b6f8a9ef-7ad1-43bc-9b00-afe83c771194"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>1821</th>\n",
              "      <td>2022-04-28</td>\n",
              "      <td>39773.828125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1822</th>\n",
              "      <td>2022-04-29</td>\n",
              "      <td>38609.824219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1823</th>\n",
              "      <td>2022-04-30</td>\n",
              "      <td>37714.875000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1824</th>\n",
              "      <td>2022-05-01</td>\n",
              "      <td>38469.093750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1825</th>\n",
              "      <td>2022-05-02</td>\n",
              "      <td>38793.300781</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             ds             y\n",
              "1821 2022-04-28  39773.828125\n",
              "1822 2022-04-29  38609.824219\n",
              "1823 2022-04-30  37714.875000\n",
              "1824 2022-05-01  38469.093750\n",
              "1825 2022-05-02  38793.300781"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_btc.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6nyrPUPQiX1B",
        "outputId": "bfbbfac7-81c3-4e26-9391-ef2fb2c4d4cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1826 entries, 0 to 1825\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype         \n",
            "---  ------  --------------  -----         \n",
            " 0   ds      1826 non-null   datetime64[ns]\n",
            " 1   y       1826 non-null   float64       \n",
            "dtypes: datetime64[ns](1), float64(1)\n",
            "memory usage: 28.7 KB\n"
          ]
        }
      ],
      "source": [
        "data_btc.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y0M0k-yLa7Nf"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LBP9PV8GjbDA",
        "outputId": "06165fac-ed3a-460a-af34-c951ed5332e9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\jonathansantos\\anaconda3\\lib\\site-packages\\fbprophet\\forecaster.py:891: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  components = components.append(new_comp)\n"
          ]
        }
      ],
      "source": [
        "m = Prophet(interval_width=0.95,daily_seasonality=False)\n",
        "model = m.fit(data_btc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h5SWBvKzZHK1"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "pAX1LUKqj2e1",
        "outputId": "4833bf7a-2c54-41a3-d8ff-10fbab7f989d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\jonathansantos\\anaconda3\\lib\\site-packages\\fbprophet\\forecaster.py:891: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  components = components.append(new_comp)\n",
            "C:\\Users\\jonathansantos\\anaconda3\\lib\\site-packages\\fbprophet\\forecaster.py:891: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  components = components.append(new_comp)\n"
          ]
        },
        {
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              "      <th>trend_upper</th>\n",
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              "      <th>yearly</th>\n",
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              "      <th>yhat</th>\n",
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              "      <th>1922</th>\n",
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              "      <td>2022-08-09</td>\n",
              "      <td>41553.002256</td>\n",
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              "      <td>2022-08-10</td>\n",
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              "             ds         trend    yhat_lower    yhat_upper   trend_lower  \\\n",
              "1921 2022-08-06  41599.258391  31139.614725  50396.776004  35834.275298   \n",
              "1922 2022-08-07  41583.839680  30585.330268  51508.871830  35706.229873   \n",
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              "1925 2022-08-10  41537.583545  30364.340805  50691.789604  35318.367241   \n",
              "\n",
              "       trend_upper  additive_terms  additive_terms_lower  \\\n",
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              "1925  47161.369861     -203.274626           -203.274626   \n",
              "\n",
              "      additive_terms_upper     weekly  weekly_lower  weekly_upper      yearly  \\\n",
              "1921           -700.293038   6.178946      6.178946      6.178946 -706.471984   \n",
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              "1925           -203.274626  48.318652     48.318652     48.318652 -251.593278   \n",
              "\n",
              "      yearly_lower  yearly_upper  multiplicative_terms  \\\n",
              "1921   -706.471984   -706.471984                   0.0   \n",
              "1922   -587.075284   -587.075284                   0.0   \n",
              "1923   -471.293492   -471.293492                   0.0   \n",
              "1924   -359.386061   -359.386061                   0.0   \n",
              "1925   -251.593278   -251.593278                   0.0   \n",
              "\n",
              "      multiplicative_terms_lower  multiplicative_terms_upper          yhat  \n",
              "1921                         0.0                         0.0  40898.965353  \n",
              "1922                         0.0                         0.0  40979.858648  \n",
              "1923                         0.0                         0.0  41113.161237  \n",
              "1924                         0.0                         0.0  41195.650564  \n",
              "1925                         0.0                         0.0  41334.308919  "
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "future = m.make_future_dataframe(periods=100, freq='D')\n",
        "forecast = m.predict(future)\n",
        "forecast.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "LJqRB-UMsMQw",
        "outputId": "22f2925e-c06c-424d-a435-2d3250b650d6"
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              "      <th>1906</th>\n",
              "      <td>2022-07-22</td>\n",
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              "      <td>41815.120355</td>\n",
              "      <td>29872.723510</td>\n",
              "      <td>47727.659924</td>\n",
              "      <td>37372.720641</td>\n",
              "      <td>45871.090061</td>\n",
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              "      <td>-2511.984544</td>\n",
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              "      <th>1908</th>\n",
              "      <td>2022-07-24</td>\n",
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              "      <th>1909</th>\n",
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              "      <td>30259.723375</td>\n",
              "      <td>48105.824917</td>\n",
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              "      <td>46011.525259</td>\n",
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              "    <tr>\n",
              "      <th>1910</th>\n",
              "      <td>2022-07-26</td>\n",
              "      <td>41768.864220</td>\n",
              "      <td>29062.349510</td>\n",
              "      <td>48626.577168</td>\n",
              "      <td>37032.020987</td>\n",
              "      <td>46113.333352</td>\n",
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              "      <td>-2146.837148</td>\n",
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              "      <th>1911</th>\n",
              "      <td>2022-07-27</td>\n",
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              "      <td>30197.943409</td>\n",
              "      <td>49406.770894</td>\n",
              "      <td>36918.454436</td>\n",
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              "      <td>48.318652</td>\n",
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              "      <td>-2016.126018</td>\n",
              "      <td>-2016.126018</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1912</th>\n",
              "      <td>2022-07-28</td>\n",
              "      <td>41738.026797</td>\n",
              "      <td>30805.836053</td>\n",
              "      <td>49415.154738</td>\n",
              "      <td>36804.887885</td>\n",
              "      <td>46234.126253</td>\n",
              "      <td>-1922.539754</td>\n",
              "      <td>-1922.539754</td>\n",
              "      <td>-1922.539754</td>\n",
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              "      <td>-39.189423</td>\n",
              "      <td>-39.189423</td>\n",
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              "      <td>-1883.350331</td>\n",
              "      <td>-1883.350331</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1913</th>\n",
              "      <td>2022-07-29</td>\n",
              "      <td>41722.608085</td>\n",
              "      <td>30336.546096</td>\n",
              "      <td>50042.542028</td>\n",
              "      <td>36710.073614</td>\n",
              "      <td>46294.133986</td>\n",
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              "      <td>-1765.761275</td>\n",
              "      <td>-1765.761275</td>\n",
              "      <td>-16.470557</td>\n",
              "      <td>-16.470557</td>\n",
              "      <td>-16.470557</td>\n",
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              "      <th>1914</th>\n",
              "      <td>2022-07-30</td>\n",
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              "      <td>30666.678457</td>\n",
              "      <td>49587.576901</td>\n",
              "      <td>36625.638787</td>\n",
              "      <td>46352.997039</td>\n",
              "      <td>-1608.498274</td>\n",
              "      <td>-1608.498274</td>\n",
              "      <td>-1608.498274</td>\n",
              "      <td>6.178946</td>\n",
              "      <td>6.178946</td>\n",
              "      <td>6.178946</td>\n",
              "      <td>-1614.677220</td>\n",
              "      <td>-1614.677220</td>\n",
              "      <td>-1614.677220</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40098.691099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1915</th>\n",
              "      <td>2022-07-31</td>\n",
              "      <td>41691.770662</td>\n",
              "      <td>30418.983594</td>\n",
              "      <td>49221.188530</td>\n",
              "      <td>36541.203959</td>\n",
              "      <td>46413.464118</td>\n",
              "      <td>-1497.090796</td>\n",
              "      <td>-1497.090796</td>\n",
              "      <td>-1497.090796</td>\n",
              "      <td>-16.905748</td>\n",
              "      <td>-16.905748</td>\n",
              "      <td>-16.905748</td>\n",
              "      <td>-1480.185048</td>\n",
              "      <td>-1480.185048</td>\n",
              "      <td>-1480.185048</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40194.679866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1916</th>\n",
              "      <td>2022-08-01</td>\n",
              "      <td>41676.351950</td>\n",
              "      <td>30481.435360</td>\n",
              "      <td>49738.679298</td>\n",
              "      <td>36456.769132</td>\n",
              "      <td>46482.586293</td>\n",
              "      <td>-1330.398532</td>\n",
              "      <td>-1330.398532</td>\n",
              "      <td>-1330.398532</td>\n",
              "      <td>16.033761</td>\n",
              "      <td>16.033761</td>\n",
              "      <td>16.033761</td>\n",
              "      <td>-1346.432292</td>\n",
              "      <td>-1346.432292</td>\n",
              "      <td>-1346.432292</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40345.953418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1917</th>\n",
              "      <td>2022-08-02</td>\n",
              "      <td>41660.933238</td>\n",
              "      <td>30960.078935</td>\n",
              "      <td>49384.850298</td>\n",
              "      <td>36346.456996</td>\n",
              "      <td>46532.429100</td>\n",
              "      <td>-1211.945103</td>\n",
              "      <td>-1211.945103</td>\n",
              "      <td>-1211.945103</td>\n",
              "      <td>2.034369</td>\n",
              "      <td>2.034369</td>\n",
              "      <td>2.034369</td>\n",
              "      <td>-1213.979472</td>\n",
              "      <td>-1213.979472</td>\n",
              "      <td>-1213.979472</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40448.988135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1918</th>\n",
              "      <td>2022-08-03</td>\n",
              "      <td>41645.514526</td>\n",
              "      <td>30820.266271</td>\n",
              "      <td>49922.870399</td>\n",
              "      <td>36218.411572</td>\n",
              "      <td>46608.014100</td>\n",
              "      <td>-1035.012114</td>\n",
              "      <td>-1035.012114</td>\n",
              "      <td>-1035.012114</td>\n",
              "      <td>48.318652</td>\n",
              "      <td>48.318652</td>\n",
              "      <td>48.318652</td>\n",
              "      <td>-1083.330766</td>\n",
              "      <td>-1083.330766</td>\n",
              "      <td>-1083.330766</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40610.502412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1919</th>\n",
              "      <td>2022-08-04</td>\n",
              "      <td>41630.095815</td>\n",
              "      <td>30360.979990</td>\n",
              "      <td>49708.999569</td>\n",
              "      <td>36090.366147</td>\n",
              "      <td>46723.458425</td>\n",
              "      <td>-994.126163</td>\n",
              "      <td>-994.126163</td>\n",
              "      <td>-994.126163</td>\n",
              "      <td>-39.189423</td>\n",
              "      <td>-39.189423</td>\n",
              "      <td>-39.189423</td>\n",
              "      <td>-954.936740</td>\n",
              "      <td>-954.936740</td>\n",
              "      <td>-954.936740</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40635.969652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1920</th>\n",
              "      <td>2022-08-05</td>\n",
              "      <td>41614.677103</td>\n",
              "      <td>31181.784567</td>\n",
              "      <td>49803.872761</td>\n",
              "      <td>35962.320722</td>\n",
              "      <td>46838.014834</td>\n",
              "      <td>-845.668907</td>\n",
              "      <td>-845.668907</td>\n",
              "      <td>-845.668907</td>\n",
              "      <td>-16.470557</td>\n",
              "      <td>-16.470557</td>\n",
              "      <td>-16.470557</td>\n",
              "      <td>-829.198350</td>\n",
              "      <td>-829.198350</td>\n",
              "      <td>-829.198350</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40769.008196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1921</th>\n",
              "      <td>2022-08-06</td>\n",
              "      <td>41599.258391</td>\n",
              "      <td>31139.614725</td>\n",
              "      <td>50396.776004</td>\n",
              "      <td>35834.275298</td>\n",
              "      <td>46937.782176</td>\n",
              "      <td>-700.293038</td>\n",
              "      <td>-700.293038</td>\n",
              "      <td>-700.293038</td>\n",
              "      <td>6.178946</td>\n",
              "      <td>6.178946</td>\n",
              "      <td>6.178946</td>\n",
              "      <td>-706.471984</td>\n",
              "      <td>-706.471984</td>\n",
              "      <td>-706.471984</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40898.965353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1922</th>\n",
              "      <td>2022-08-07</td>\n",
              "      <td>41583.839680</td>\n",
              "      <td>30585.330268</td>\n",
              "      <td>51508.871830</td>\n",
              "      <td>35706.229873</td>\n",
              "      <td>46994.638840</td>\n",
              "      <td>-603.981032</td>\n",
              "      <td>-603.981032</td>\n",
              "      <td>-603.981032</td>\n",
              "      <td>-16.905748</td>\n",
              "      <td>-16.905748</td>\n",
              "      <td>-16.905748</td>\n",
              "      <td>-587.075284</td>\n",
              "      <td>-587.075284</td>\n",
              "      <td>-587.075284</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40979.858648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1923</th>\n",
              "      <td>2022-08-08</td>\n",
              "      <td>41568.420968</td>\n",
              "      <td>30708.154862</td>\n",
              "      <td>49971.668097</td>\n",
              "      <td>35578.184449</td>\n",
              "      <td>47050.820459</td>\n",
              "      <td>-455.259731</td>\n",
              "      <td>-455.259731</td>\n",
              "      <td>-455.259731</td>\n",
              "      <td>16.033761</td>\n",
              "      <td>16.033761</td>\n",
              "      <td>16.033761</td>\n",
              "      <td>-471.293492</td>\n",
              "      <td>-471.293492</td>\n",
              "      <td>-471.293492</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41113.161237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1924</th>\n",
              "      <td>2022-08-09</td>\n",
              "      <td>41553.002256</td>\n",
              "      <td>31433.406927</td>\n",
              "      <td>50943.436549</td>\n",
              "      <td>35450.139024</td>\n",
              "      <td>47106.095160</td>\n",
              "      <td>-357.351692</td>\n",
              "      <td>-357.351692</td>\n",
              "      <td>-357.351692</td>\n",
              "      <td>2.034369</td>\n",
              "      <td>2.034369</td>\n",
              "      <td>2.034369</td>\n",
              "      <td>-359.386061</td>\n",
              "      <td>-359.386061</td>\n",
              "      <td>-359.386061</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41195.650564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1925</th>\n",
              "      <td>2022-08-10</td>\n",
              "      <td>41537.583545</td>\n",
              "      <td>30364.340805</td>\n",
              "      <td>50691.789604</td>\n",
              "      <td>35318.367241</td>\n",
              "      <td>47161.369861</td>\n",
              "      <td>-203.274626</td>\n",
              "      <td>-203.274626</td>\n",
              "      <td>-203.274626</td>\n",
              "      <td>48.318652</td>\n",
              "      <td>48.318652</td>\n",
              "      <td>48.318652</td>\n",
              "      <td>-251.593278</td>\n",
              "      <td>-251.593278</td>\n",
              "      <td>-251.593278</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41334.308919</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             ds         trend    yhat_lower    yhat_upper   trend_lower  \\\n",
              "1906 2022-07-22  41830.539067  29625.863892  48310.114188  37486.596440   \n",
              "1907 2022-07-23  41815.120355  29872.723510  47727.659924  37372.720641   \n",
              "1908 2022-07-24  41799.701643  30014.680500  48743.046842  37259.154090   \n",
              "1909 2022-07-25  41784.282932  30259.723375  48105.824917  37145.587538   \n",
              "1910 2022-07-26  41768.864220  29062.349510  48626.577168  37032.020987   \n",
              "1911 2022-07-27  41753.445508  30197.943409  49406.770894  36918.454436   \n",
              "1912 2022-07-28  41738.026797  30805.836053  49415.154738  36804.887885   \n",
              "1913 2022-07-29  41722.608085  30336.546096  50042.542028  36710.073614   \n",
              "1914 2022-07-30  41707.189373  30666.678457  49587.576901  36625.638787   \n",
              "1915 2022-07-31  41691.770662  30418.983594  49221.188530  36541.203959   \n",
              "1916 2022-08-01  41676.351950  30481.435360  49738.679298  36456.769132   \n",
              "1917 2022-08-02  41660.933238  30960.078935  49384.850298  36346.456996   \n",
              "1918 2022-08-03  41645.514526  30820.266271  49922.870399  36218.411572   \n",
              "1919 2022-08-04  41630.095815  30360.979990  49708.999569  36090.366147   \n",
              "1920 2022-08-05  41614.677103  31181.784567  49803.872761  35962.320722   \n",
              "1921 2022-08-06  41599.258391  31139.614725  50396.776004  35834.275298   \n",
              "1922 2022-08-07  41583.839680  30585.330268  51508.871830  35706.229873   \n",
              "1923 2022-08-08  41568.420968  30708.154862  49971.668097  35578.184449   \n",
              "1924 2022-08-09  41553.002256  31433.406927  50943.436549  35450.139024   \n",
              "1925 2022-08-10  41537.583545  30364.340805  50691.789604  35318.367241   \n",
              "\n",
              "       trend_upper  additive_terms  additive_terms_lower  \\\n",
              "1906  45784.711122    -2648.529844          -2648.529844   \n",
              "1907  45871.090061    -2511.984544          -2511.984544   \n",
              "1908  45957.129687    -2415.635559          -2415.635559   \n",
              "1909  46011.525259    -2258.624994          -2258.624994   \n",
              "1910  46113.333352    -2144.802779          -2144.802779   \n",
              "1911  46173.905483    -1967.807366          -1967.807366   \n",
              "1912  46234.126253    -1922.539754          -1922.539754   \n",
              "1913  46294.133986    -1765.761275          -1765.761275   \n",
              "1914  46352.997039    -1608.498274          -1608.498274   \n",
              "1915  46413.464118    -1497.090796          -1497.090796   \n",
              "1916  46482.586293    -1330.398532          -1330.398532   \n",
              "1917  46532.429100    -1211.945103          -1211.945103   \n",
              "1918  46608.014100    -1035.012114          -1035.012114   \n",
              "1919  46723.458425     -994.126163           -994.126163   \n",
              "1920  46838.014834     -845.668907           -845.668907   \n",
              "1921  46937.782176     -700.293038           -700.293038   \n",
              "1922  46994.638840     -603.981032           -603.981032   \n",
              "1923  47050.820459     -455.259731           -455.259731   \n",
              "1924  47106.095160     -357.351692           -357.351692   \n",
              "1925  47161.369861     -203.274626           -203.274626   \n",
              "\n",
              "      additive_terms_upper     weekly  weekly_lower  weekly_upper  \\\n",
              "1906          -2648.529844 -16.470557    -16.470557    -16.470557   \n",
              "1907          -2511.984544   6.178946      6.178946      6.178946   \n",
              "1908          -2415.635559 -16.905748    -16.905748    -16.905748   \n",
              "1909          -2258.624994  16.033761     16.033761     16.033761   \n",
              "1910          -2144.802779   2.034369      2.034369      2.034369   \n",
              "1911          -1967.807366  48.318652     48.318652     48.318652   \n",
              "1912          -1922.539754 -39.189423    -39.189423    -39.189423   \n",
              "1913          -1765.761275 -16.470557    -16.470557    -16.470557   \n",
              "1914          -1608.498274   6.178946      6.178946      6.178946   \n",
              "1915          -1497.090796 -16.905748    -16.905748    -16.905748   \n",
              "1916          -1330.398532  16.033761     16.033761     16.033761   \n",
              "1917          -1211.945103   2.034369      2.034369      2.034369   \n",
              "1918          -1035.012114  48.318652     48.318652     48.318652   \n",
              "1919           -994.126163 -39.189423    -39.189423    -39.189423   \n",
              "1920           -845.668907 -16.470557    -16.470557    -16.470557   \n",
              "1921           -700.293038   6.178946      6.178946      6.178946   \n",
              "1922           -603.981032 -16.905748    -16.905748    -16.905748   \n",
              "1923           -455.259731  16.033761     16.033761     16.033761   \n",
              "1924           -357.351692   2.034369      2.034369      2.034369   \n",
              "1925           -203.274626  48.318652     48.318652     48.318652   \n",
              "\n",
              "           yearly  yearly_lower  yearly_upper  multiplicative_terms  \\\n",
              "1906 -2632.059287  -2632.059287  -2632.059287                   0.0   \n",
              "1907 -2518.163490  -2518.163490  -2518.163490                   0.0   \n",
              "1908 -2398.729812  -2398.729812  -2398.729812                   0.0   \n",
              "1909 -2274.658755  -2274.658755  -2274.658755                   0.0   \n",
              "1910 -2146.837148  -2146.837148  -2146.837148                   0.0   \n",
              "1911 -2016.126018  -2016.126018  -2016.126018                   0.0   \n",
              "1912 -1883.350331  -1883.350331  -1883.350331                   0.0   \n",
              "1913 -1749.290718  -1749.290718  -1749.290718                   0.0   \n",
              "1914 -1614.677220  -1614.677220  -1614.677220                   0.0   \n",
              "1915 -1480.185048  -1480.185048  -1480.185048                   0.0   \n",
              "1916 -1346.432292  -1346.432292  -1346.432292                   0.0   \n",
              "1917 -1213.979472  -1213.979472  -1213.979472                   0.0   \n",
              "1918 -1083.330766  -1083.330766  -1083.330766                   0.0   \n",
              "1919  -954.936740   -954.936740   -954.936740                   0.0   \n",
              "1920  -829.198350   -829.198350   -829.198350                   0.0   \n",
              "1921  -706.471984   -706.471984   -706.471984                   0.0   \n",
              "1922  -587.075284   -587.075284   -587.075284                   0.0   \n",
              "1923  -471.293492   -471.293492   -471.293492                   0.0   \n",
              "1924  -359.386061   -359.386061   -359.386061                   0.0   \n",
              "1925  -251.593278   -251.593278   -251.593278                   0.0   \n",
              "\n",
              "      multiplicative_terms_lower  multiplicative_terms_upper          yhat  \n",
              "1906                         0.0                         0.0  39182.009223  \n",
              "1907                         0.0                         0.0  39303.135811  \n",
              "1908                         0.0                         0.0  39384.066084  \n",
              "1909                         0.0                         0.0  39525.657938  \n",
              "1910                         0.0                         0.0  39624.061441  \n",
              "1911                         0.0                         0.0  39785.638142  \n",
              "1912                         0.0                         0.0  39815.487043  \n",
              "1913                         0.0                         0.0  39956.846810  \n",
              "1914                         0.0                         0.0  40098.691099  \n",
              "1915                         0.0                         0.0  40194.679866  \n",
              "1916                         0.0                         0.0  40345.953418  \n",
              "1917                         0.0                         0.0  40448.988135  \n",
              "1918                         0.0                         0.0  40610.502412  \n",
              "1919                         0.0                         0.0  40635.969652  \n",
              "1920                         0.0                         0.0  40769.008196  \n",
              "1921                         0.0                         0.0  40898.965353  \n",
              "1922                         0.0                         0.0  40979.858648  \n",
              "1923                         0.0                         0.0  41113.161237  \n",
              "1924                         0.0                         0.0  41195.650564  \n",
              "1925                         0.0                         0.0  41334.308919  "
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "forecast.tail(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "e5nTcHMNsaW2",
        "outputId": "88346a42-6a61-4129-f954-748f2784044e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>yhat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017-05-03</td>\n",
              "      <td>3360.320181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-05-04</td>\n",
              "      <td>3314.973423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2017-05-05</td>\n",
              "      <td>3364.141931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017-05-06</td>\n",
              "      <td>3396.068960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-05-07</td>\n",
              "      <td>3363.994732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1921</th>\n",
              "      <td>2022-08-06</td>\n",
              "      <td>40898.965353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1922</th>\n",
              "      <td>2022-08-07</td>\n",
              "      <td>40979.858648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1923</th>\n",
              "      <td>2022-08-08</td>\n",
              "      <td>41113.161237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1924</th>\n",
              "      <td>2022-08-09</td>\n",
              "      <td>41195.650564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1925</th>\n",
              "      <td>2022-08-10</td>\n",
              "      <td>41334.308919</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1926 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             ds          yhat\n",
              "0    2017-05-03   3360.320181\n",
              "1    2017-05-04   3314.973423\n",
              "2    2017-05-05   3364.141931\n",
              "3    2017-05-06   3396.068960\n",
              "4    2017-05-07   3363.994732\n",
              "...         ...           ...\n",
              "1921 2022-08-06  40898.965353\n",
              "1922 2022-08-07  40979.858648\n",
              "1923 2022-08-08  41113.161237\n",
              "1924 2022-08-09  41195.650564\n",
              "1925 2022-08-10  41334.308919\n",
              "\n",
              "[1926 rows x 2 columns]"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "forecast[['ds','yhat']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i7uynb3NRd1l"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ro-NNZycjM5Y",
        "outputId": "d9a2e70b-a342-4cc3-800c-35f8bae4daf8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE: 3163.466\n",
            "r^2 score = 0.9383909852629867\n"
          ]
        }
      ],
      "source": [
        "train = data_btc['y']\n",
        "test = forecast['yhat'][:1826].values\n",
        "\n",
        "mae = mean_absolute_error(train, test)\n",
        "print('MAE: %.3f' % mae)\n",
        "\n",
        "\n",
        "print(f'r^2 score = {r2_score(train,test)}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "wSB8_fBLtEFK",
        "outputId": "9737043c-0c16-44e5-ebe8-fac540869371"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "data_btc['y'].plot(color='b', linestyle = '-')\n",
        "forecast['yhat'].plot(color='r', linestyle = '-')\n",
        "plot = m.plot(forecast)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "Oe0fehyjtbyw",
        "outputId": "ef59f7c3-92b7-415c-8e18-ceb2e3bf560e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\jonathansantos\\anaconda3\\lib\\site-packages\\fbprophet\\forecaster.py:891: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  components = components.append(new_comp)\n",
            "C:\\Users\\jonathansantos\\anaconda3\\lib\\site-packages\\fbprophet\\forecaster.py:891: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  components = components.append(new_comp)\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot2 = m.plot_components(forecast)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uamQDUM8f4Xy"
      },
      "outputs": [],
      "source": [
        "#pip install dash"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aAN24b_Ef4Xz"
      },
      "outputs": [],
      "source": [
        "#pip install dash_bootstrap_components"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rDWopWYbevPJ",
        "outputId": "be8ae370-d69b-4311-945e-89414798f9fe"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-28-0ce921b7a48c>:2: UserWarning: \n",
            "The dash_html_components package is deprecated. Please replace\n",
            "`import dash_html_components as html` with `from dash import html`\n",
            "  import dash_html_components as html\n",
            "<ipython-input-28-0ce921b7a48c>:4: UserWarning: \n",
            "The dash_core_components package is deprecated. Please replace\n",
            "`import dash_core_components as dcc` with `from dash import dcc`\n",
            "  import dash_core_components as dcc\n"
          ]
        }
      ],
      "source": [
        "import dash\n",
        "import dash_html_components as html\n",
        "import plotly.graph_objects as go\n",
        "import dash_core_components as dcc\n",
        "import plotly.express as px\n",
        "from dash.dependencies import Input, Output\n",
        "\n",
        "\n",
        "app = dash.Dash()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CFV9c5Ief4X2"
      },
      "outputs": [],
      "source": [
        "app.layout = html.Div([\n",
        "\n",
        "html.Div([\n",
        "    html.Img( src = 'https://freepngimg.com/thumb/bitcoin/59620-cryptocurrency-bitcoin-free-frame.png',\n",
        "             style = {'width':'150px','display':'block', 'margin-left':'auto', 'margin-right':'auto'}),\n",
        "    \n",
        "    html.H1(id = 'H1', children = 'Crypto Prediction', style = {'textAlign':'center',\\\n",
        "                                            'marginTop':40,'marginBottom':40})\n",
        "    ]),\n",
        "\n",
        "    \n",
        "html.Div([       \n",
        "        \n",
        "        dcc.Dropdown( id = 'dropdown',\n",
        "        options = [\n",
        "            {'label':'Prophet', 'value':'Prof' },\n",
        "            {'label': 'ARIMA', 'value':'arm'},\n",
        "            {'label': 'Smoothing', 'value':'smt'},\n",
        "            ],\n",
        "        value = 'arm', style = {'background-color': 'lightblue'}),\n",
        "        dcc.Graph(id = 'bar_plot')\n",
        "        \n",
        "    ])\n",
        "    \n",
        "   \n",
        "    ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zdeQhzAUf4X8"
      },
      "outputs": [],
      "source": [
        "@app.callback(Output(component_id='bar_plot', component_property= 'figure'),\n",
        "              [Input(component_id='dropdown', component_property= 'value')])\n",
        "\n",
        "def graph_update(dropdown_value):\n",
        "    print(dropdown_value)\n",
        "    \n",
        "    if dropdown_value == 'Prof':\n",
        "        fig = go.Figure()\n",
        "        fig.add_trace(go.Scatter(x = forecast['ds'],y = forecast['yhat'],\\\n",
        "                     line = dict(color = 'firebrick', width = 4), name = 'prophet'\n",
        "                     ))\n",
        "        fig.add_trace(go.Scatter(x = data_btc['ds'],y = data_btc['y'],\\\n",
        "                     line = dict(color = 'blue', width = 4), name = 'prophet'\n",
        "                     ))\n",
        "        fig.update_layout(title = 'Prices over time',\n",
        "                      xaxis_title = 'Dates',\n",
        "                      yaxis_title = 'Prices'\n",
        "                      )\n",
        "        return fig\n",
        "\n",
        "    elif dropdown_value == 'arm':\n",
        "\n",
        "        fig_two = go.Figure([go.Scatter(x = data_btc['ds'], y = data_btc['y'],\\\n",
        "                     line = dict(color = 'firebrick', width = 4))\n",
        "                     ])\n",
        "        fig_two.update_layout(title = 'Prices over time',\n",
        "                      xaxis_title = 'Dates',\n",
        "                      yaxis_title = 'Prices'\n",
        "                      )    \n",
        "\n",
        "        return fig_two  \n",
        "    elif dropdown_value == 'smt':\n",
        "\n",
        "        fig_three = go.Figure([go.Scatter(x = forecast['ds'],y = forecast['yhat'],\\\n",
        "                     line = dict(color = 'firebrick', width = 4))\n",
        "                     ])\n",
        "        fig_three.update_layout(title = 'Prices over time',\n",
        "                      xaxis_title = 'Dates',\n",
        "                      yaxis_title = 'Prices'\n",
        "                      )    \n",
        "\n",
        "        return fig_three\n",
        "    \n",
        "    else:\n",
        "        print('test')\n",
        "        "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_RSykuecf4X_",
        "outputId": "aac9f884-860a-4eb6-cc08-e1ca9b12cba9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:__main__:Dash is running on http://127.0.0.1:8050/\n",
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " * Serving Flask app \"__main__\" (lazy loading)\n",
            " * Environment: production\n",
            "   WARNING: This is a development server. Do not use it in a production deployment.\n",
            "   Use a production WSGI server instead.\n",
            " * Debug mode: off\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:werkzeug: * Running on http://127.0.0.1:8050/ (Press CTRL+C to quit)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Prof\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:werkzeug:127.0.0.1 - - [02/May/2022 12:28:03] \"\u001b[37mPOST /_dash-update-component HTTP/1.1\u001b[0m\" 200 -\n"
          ]
        }
      ],
      "source": [
        "\n",
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      "source": [
        ""
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