Prophet_Model_(2)_(1).ipynb

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        "<a href=\"https://colab.research.google.com/github/SergJohn/Crypto-Prediction-2022-Project/blob/main/Prophet_Model_(2)_(1).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": "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')\n",
        "data_et = pdr.get_data_yahoo('ETH-USD')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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              "      <th>2017-11-09</th>\n",
              "      <td>329.451996</td>\n",
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              "      <th>2017-11-11</th>\n",
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              "      <th>2017-11-13</th>\n",
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              "      <td>307.024994</td>\n",
              "      <td>307.024994</td>\n",
              "      <td>316.716003</td>\n",
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            "text/plain": [
              "                  High         Low        Open       Close      Volume  \\\n",
              "Date                                                                     \n",
              "2017-11-09  329.451996  307.056000  308.644989  320.884003   893249984   \n",
              "2017-11-10  324.717987  294.541992  320.670990  299.252991   885985984   \n",
              "2017-11-11  319.453003  298.191986  298.585999  314.681000   842300992   \n",
              "2017-11-12  319.153015  298.513000  314.690002  307.907990  1613479936   \n",
              "2017-11-13  328.415009  307.024994  307.024994  316.716003  1041889984   \n",
              "\n",
              "             Adj Close  \n",
              "Date                    \n",
              "2017-11-09  320.884003  \n",
              "2017-11-10  299.252991  \n",
              "2017-11-11  314.681000  \n",
              "2017-11-12  307.907990  \n",
              "2017-11-13  316.716003  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "data_et.head()\n"
      ]
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            "text/plain": [
              "<bound method DataFrame.info of                     High           Low          Open         Close  \\\n",
              "Date                                                                 \n",
              "2017-05-07   1596.719971   1559.760010   1579.469971   1596.709961   \n",
              "2017-05-08   1723.349976   1596.920044   1596.920044   1723.349976   \n",
              "2017-05-09   1833.489990   1716.300049   1723.890015   1755.359985   \n",
              "2017-05-10   1788.439941   1719.099976   1756.520020   1787.130005   \n",
              "2017-05-11   1873.930054   1755.349976   1780.369995   1848.569946   \n",
              "...                  ...           ...           ...           ...   \n",
              "2022-05-02  39074.972656  38156.562500  38472.187500  38529.328125   \n",
              "2022-05-03  38629.996094  37585.621094  38528.109375  37750.453125   \n",
              "2022-05-04  39902.949219  37732.058594  37748.011719  39698.371094   \n",
              "2022-05-05  39789.281250  35856.515625  39695.746094  36575.140625   \n",
              "2022-05-06  36607.585938  35482.132812  36550.769531  36281.000000   \n",
              "\n",
              "                 Volume     Adj Close  \n",
              "Date                                   \n",
              "2017-05-07   1080029952   1596.709961  \n",
              "2017-05-08   1340320000   1723.349976  \n",
              "2017-05-09   1167920000   1755.359985  \n",
              "2017-05-10    915723008   1787.130005  \n",
              "2017-05-11    799489984   1848.569946  \n",
              "...                 ...           ...  \n",
              "2022-05-02  32922642426  38529.328125  \n",
              "2022-05-03  27326943244  37750.453125  \n",
              "2022-05-04  36754404490  39698.371094  \n",
              "2022-05-05  43106256317  36575.140625  \n",
              "2022-05-06  51464740864  36281.000000  \n",
              "\n",
              "[1826 rows x 6 columns]>"
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          "execution_count": 9,
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              "<bound method DataFrame.info of                    High          Low         Open        Close       Volume  \\\n",
              "Date                                                                          \n",
              "2017-11-09   329.451996   307.056000   308.644989   320.884003    893249984   \n",
              "2017-11-10   324.717987   294.541992   320.670990   299.252991    885985984   \n",
              "2017-11-11   319.453003   298.191986   298.585999   314.681000    842300992   \n",
              "2017-11-12   319.153015   298.513000   314.690002   307.907990   1613479936   \n",
              "2017-11-13   328.415009   307.024994   307.024994   316.716003   1041889984   \n",
              "...                 ...          ...          ...          ...          ...   \n",
              "2022-05-02  2874.145996  2785.522949  2827.614014  2857.410400  18609741545   \n",
              "2022-05-03  2859.193359  2762.118652  2857.152344  2783.476318  13026093219   \n",
              "2022-05-04  2956.686768  2779.273682  2783.131104  2940.644775  18186749944   \n",
              "2022-05-05  2948.960693  2704.916260  2940.226562  2749.213135  22642925048   \n",
              "2022-05-06  2753.658447  2645.335205  2746.350586  2711.370850  27611660288   \n",
              "\n",
              "              Adj Close  \n",
              "Date                     \n",
              "2017-11-09   320.884003  \n",
              "2017-11-10   299.252991  \n",
              "2017-11-11   314.681000  \n",
              "2017-11-12   307.907990  \n",
              "2017-11-13   316.716003  \n",
              "...                 ...  \n",
              "2022-05-02  2857.410400  \n",
              "2022-05-03  2783.476318  \n",
              "2022-05-04  2940.644775  \n",
              "2022-05-05  2749.213135  \n",
              "2022-05-06  2711.370850  \n",
              "\n",
              "[1640 rows x 6 columns]>"
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          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "data_et.info"
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    {
      "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": {
        "id": "0n8OcPz2xhlZ"
      },
      "outputs": [],
      "source": [
        "data_et=data_et.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HN_bYr2Srn69",
        "outputId": "d245fc4f-430e-44ca-96bc-4510efd620b1"
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      "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": "9xOVtuANxhlb"
      },
      "outputs": [],
      "source": [
        "data_et.drop(['High', 'Low', 'Open', 'Volume', 'Adj Close'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pE2RWG-Pzwyy"
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      "outputs": [],
      "source": [
        ""
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              "           Date         Close\n",
              "1821 2022-05-02  38529.328125\n",
              "1822 2022-05-03  37750.453125\n",
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              "           Date        Close\n",
              "1635 2022-05-02  2857.410400\n",
              "1636 2022-05-03  2783.476318\n",
              "1637 2022-05-04  2940.644775\n",
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      "execution_count": null,
      "metadata": {
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        "data_btc.columns = ['ds','y']"
      ]
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      "execution_count": null,
      "metadata": {
        "id": "4kirgkcAxhld"
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iPYUR6SnUNjjZut9qg26PgSvN1qCNMXONMQOMMRlYN/8WG2OuBRYAs+3dZgPv29sLgFl2z4whWDcDV9rNIBUiMtFuX77O5xjXa11unyM0E64qpVrEGOO+oTcx068F8qhsLz7s9bjUI9k9ceXYNj1XIL6z4QXqOpc1uLtf2UUnBjflarCOph/0o8BbInIjsAe4AsAYs0lE3gI2Aw3ALcYY1/eXm4GXgUTgY/sH4EXgNRHJxao5zzqKuJRS7aCqzsHDH20FoGti2/b9rfZp4iirtm4aPnXVOC4Oct7p1vBtY01JOJIa1/zhAvaVVTPAZ2ALWO3g7/38NC59ZhlXjB/g9/zRCipBG2OWAEvs7YPA5Eb2ewh4KEB5NnBCgPIa7ASvlIpMD/xnM92SYimtqm92ZF9jXr1hAtf9e6VfuW+CdjUXdGvjD4LGrLP7XAM8OeskuiXF8bOzM5kyqg89kuPokRzX6LHjBnWnX9eEdmmW0ZGESqkWmZedx+TjerO/vIbYViz8CnDGsF4By1/8ZicLNxfywuws0lMT3E0c3ZMaT4ztYfXvz6enPUfH3Atb3s4+dmA3iipaN690UzRBK6VaZGCPRCrrGkiOa33aiGqk/3RpVT2lVWWc+vAidj4y3T2hUrek0NSgXZqqKTfliStPatV8IM3RyZKUUi3SNTGWlTtLSIpv20R05nDvWnVRRS1lriaOECXo6yYNJj013m/4d0u1R3IGTdBKqUb4tqlu3FuO08Cy7QeP6nXfuXkSV586yP3Yd9WUUx9exO6SSmKihC7xofmS/8DME1hxz/khOVcwNEErpQJqbPBIXZCrcvsaP7gHD186xj2XRqDX21RQTrek2FbXaDsLTdBKqYByi6y+yb6DUq6aMCjQ7kH74NYzuP/i0QE/CNbuKW3zrnwdkSZopVRAv3vPWmVkxU7vQb0PXuLXU7ZVhqenMPu0DEo95uF4ctZJ7u1Q9+CIRJqglVIBjbTnmfjxxMFe5a2dya4xPTwS8cyT+ru3Q92DIxJpglZKBdSnawJxMVGcNaJ9JyZ76SenMHV0Ou/fcjoA4wZZczu3Zoa7zkYTtFIqoNp6Jwn2jby7po1st/P065bIv36cxVh70v37Lx4NwOHa8M5wFwl0oIpSKqDaBod7teyfnzOMSZk9OXC45Styt1a3RKvJw3fNwGORJmilVEDl1Q1eAzDGDfKfza09DOyRyO9nHM+FY/qG5HyRTBO0UiqgrfvLGZme0vyObUxE+K8zM0N+3kik3yGUUn6cTkPeoepWr/Wn2oYmaKWUn8KKGuoanAzq4T8HsgodTdBKKT8b95YDMLinJuhw0gStlPLz+KfWyikZPbWJI5w0QSul/Azr3QWAgdrEEVaaoJVSfgRhaJrWnsNNE7RSyosxhtLqOlICrGytQkv7QSulvFz74gq+yT1Iry46m1y4aQ1aKeXlm1xrxZRQDOtWTdMErZQKaN6cieEO4ZinTRxKKTdjDFFizQF9ambPcIdzzGu2Bi0iA0XkCxHZIiKbROSXdnkPEflMRHLs3909jpkrIrkisk1EpnqUjxeRDfZzT4m94JiIxIvIPLt8hYhktP1bVUo1p7regdNYU4Cq8GtJE0cDcIcx5nhgInCLiIwC7gYWGWOGA4vsx9jPzQJGA9OAZ0TENSXWs8AcYLj9M80uvxE4ZIwZBvwNeKwN3ptSKkiHaxoA6JKgX64jQbMJ2hizzxizxt6uALYA/YGZwCv2bq8Al9jbM4E3jTG1xpidQC4wQUT6AqnGmOXGGAO86nOM67XmA5PlWF/OV6kQMsbgdBoqau0EHa8JOhIE9Vewmx7GASuAdGPMPrCSuIj0tnfrD3zrcVi+XVZvb/uWu47Js1+rQUTKgJ7AAZ/zz8GqgTNoUNusLBwOb6zcw5JtRfzrx1nhDkUp6h1Obv6fNXy+pZAMe+6NFK1BR4QW/xVEpAvwDnC7Maa8iQpuoCdME+VNHeNdYMxzwHMAWVlZfs93FHPftVZL3nmgkiE6naMKs/P+uoS8kmoAdh2sAmB0v67hDEnZWtTNTkRisZLz68aYd+3iQrvZAvt3kV2eDwz0OHwAUGCXDwhQ7nWMiMQAXQHvtd47iX1l1e7twvKaMEaiFJRU1rmTs6f01IQwRKN8taQXhwAvAluMMU94PLUAmG1vzwbe9yifZffMGIJ1M3Cl3RxSISIT7de8zucY12tdDiy226k7FYfTMOmRxe7HZdX15BRW8MTCbXTCt6s6gC+/L/Ir+8W5w8IQiQqkJU0cpwM/BjaIyDq77B7gUeAtEbkR2ANcAWCM2SQibwGbsXqA3GKMcS3PezPwMpAIfGz/gPUB8JqI5GLVnGcd5fuKSLUN3qsUl1XVc+Mr2ewpqeJHEwbRX7s2qRDbdaDKvX3WiDRevWFCGKNRvppN0MaYrwncRgwwuZFjHgIeClCeDZwQoLwGO8F3ZrX1Tq/Hq3aVUFJpDactrqjVBK1Cqt7h5MlFOQA8MHM054zo3cwRKtT0Vm0I1TZ4J+i3Vx/p1FJcURvqcNQxrLrOwX+9usr9+LpJGeELRjVK5+IIId8mDk+rdnXKe6IqQr29Os89KdKCX5we5mhUYzRBh5CrBj1hSA+/5577akeow1HHsLKqevf2iQO6hTES1RRN0CHkaoP+6ZmZAZ//bHNhKMNRx6hDlXXuCsHmB6Y2s7cKJ03QIeRq4oiPieKkgf61li37ykMdUqfR4HC6b7iqxhljGPenz9xDupPi9DZUJNMEHUKuJo74mCj+75bTmZBhNXW8/JNTAHjis+/JLToctvg6st+9t5GT//QZ+8qqWbRFv4k05s+fbnNvf/7rs8MYiWoJTdAh5K5Bx1qT+z0/O4t3bj6Nc0Ye6d70zpp8nE4dtBKsedl5AEx6ZDE3vpJNUYU1StMYwwtLd5BTWBHO8NpNTb2DtXsO8Zu31zPq3k/YW1rN29l5jf4bcn1L23T/VPfK3Spy6febENp7yBpS2y3RWoyza2Is4wd399rn2SXb6RIfwy3nDiO36DCbCsrYX1bDnpIqHrp0TMhj7qh+8tIqPrztTJ5alMvfPv+eBz/cwrYHpxEfE938wR1EdZ2D4+/9xKvs9EetkapdE2OZMroPxhhEhOo6B7e9uZYl24qZcWJfknW2ug5B/0oh9O7avWT2SmawPWOYp9hood5h1XoWbi7kprOHcv4TX3rt84cfjCIhtvMkmPa0qaCc1bsPUVJ5pH/5OY8vYf7Np+F0GuJjo+id0rHnm7j+pZWNPldYXsPF//ia7/LLOKF/Khv3Hrm/MVv7PHcYmqBDpLK2gbV7SvnZ2ZkEmgnw69+exz3vbmDR1iLW55VSUOo/gU1BaTWZafq11Ff+oaqA5T98dhmZvZLpmhhLWXU9+8pq3DVMl6smDGTu9ONJTYhts3g2F5Qz/amljB/cnf+58VQS41r2oVpWXU9X+9tVZW0DH27YxxXjBwT89wKwZs8hAJ6/LosLRqUDUFFTz5j7FvKH9ze593Ml5+G9u/DhbWcSF6Mtmx2FJugQ+fviXAAyegaeXjQ9NYEXrz+FjLs/BGBtXqnfPq8s28X9M/1GyvOPxTks2VZM9u5DzL9pElkZ/v2sO7PNBVYCeuaakxk3qJvXhFQ7DlRy/8Wj+eOCTQGPfWNlHj2T47lz6kh32ca9ZSzNOcAZw3pxQv/URhOkL4fT8PKyXfzpg80ArN59iOPv/YRN909ttkkhr6SKM//8BQADuieSbzeH3TX/O0TgjGG9+O2040iIjSIpLoaqOgf1DsP1p2W4kzNAiscHzdTR6SzZVsyvLhhBj+Q4po/pq8m5g9EEHSKuaUYvHz+gyf1umzycpxblcMdb1rxU08f0YVhaF55anMsry3dT5zBcc+ogTuhvzddbU+/gLwu/dx9/+T+Xs+Ph6URFde4FaXYeqCQ5PppeyfHMeW01AKcP7UXXpFgev/xEfjP/O/e+F4xK5+Kx/eiSEMO972/k2x0lvHPzafRIjuP8J75ke7F3z5kf/P1r4Mi6a98/eGGTia2sqp5/fbWd/EPVLFhf4Pf86D9+ys/OymTu9OMDHt/gcHp9gLiSs4sxsDTnAEtzvvY7duZJ/fzKdj06w932rDo2TdAhUFnbwPvrCjjvuN7ERjddg+ljz8Prao9+5prxADxl18DfWLmHN1buYecj06lzOJn81y/9XiPzno94+uqTmXFi3ybP9fdFOfTpmsCl4/oT00xckSL/UBWrdpXwq3nrAWv1aYCR6Sl0TbJqj5ePH8BFY/sRbX9IeV7zhy4Zgwju5NUjKY6PN+7nrD9/wZ6SKm44fYjfOUf8/mO2PDAtYFNFdZ2DKf/9JYXlR9q677hgBD8/dxi1DQ5G3fspAP/6age/umBEwHsIb6zcw+Kt/tN+/vv6LPqkJrKt0GpP//C7fRyqqmfsgK5kpnXhR6cMZNyg7n7HAZqcOwlN0O2svKaeE+9bCFhfXZtzRdYA7nlvg1/5l785h7MfX+J+/PqKPWzdX85eu61665+mUVxR6/6afMv/riExLot1eWX87KxMv6/YZdX1/PWz7+0YG7jxDP/EFGkqauo547EvvMpe+3Y3AO97zCchIo3eTPX9ZjE+ozsrd5Wwp8Rqx/73NzsBuPmcoVw3aTA3vbaa9fll3Pn2eqaMTmdU31SGp6dQU+8gITaaq1/41is5PzBztHvioaS4GObfNInL/7kcgHfX7OXqU/2Xatu63+oC+MZPJ5JXUsWME/uy62Cle1WTUf1SuXTcAB68RHvxHGuko04Un5WVZbKzs8MdRqM8E7PLhvumeLURNsbVDr3mDxfQIznOXT5/dT7FFbU89slWr/3f+tkk9/weVXUNXPbMMvd/eoAfZQ3ksctPBOzFQY31Nd5z5KIrtu3Fh7lvwSZSEmLctfdI8fI3O7nvP1b77rDeXeiaGMvq3Ye4aGw//n7VuFa9ZlVdA2+uzKPe4aRft0RufWMtAPdfPJrZp2UAkDn3Q5rrmv7OzZMYPzhw27/rxt1lJ/fniStPAuDKfy3nu/xSUhNiKaqoJWtwd+bffFqr3oPq2ERktTEm4AKlWoNuJ2+tyvN6vP7eliVnT57JGY60X3sm6IvG9vOafCkpLoZ3bj6Na19cwdo91o3Gedl5zMvO476LRvHnT7dRVWcNmDmuTwp3ThnJf72azRifDxOAe9/fyNKcA+w8UMmME/tyqLKOpLhoJgzpQUJsNAmx0Tidhh+M7ReSVaCfWbIdgNOH9eTln0wgJkowxr9WHIykuBhu8Pj2MO2EPizeWsTZI9LcZZeOG8A7a/IDHQ7Ap7efxcg+KY0+n5IQy9C0ZGobnMx99zveWHnk30ZNvVX7fubak1v9HlTnpTXoNuR0Gl5dvotFW4tYmnOAnslxHLTnh9j16IwWv05ReQ3xMdHuNlVf6/NKuf6llZTXNLB87nlN9ue98+31zF8dOLksvuNsMtO6uGvsLldmDeCt7MYTkq9Zpwzk0R+e2OL9W6OoooZzH1+C08CWP01r13MFUtvgcA9yefijLZw4oCsXntCXfWXVDOju36/d1/Qnl7LZZ66VzF7JREcJj1w25pjreaOOaKoGrQm6Dc1btYffvnOk/fiG04dQWF7DyD4p3DZ5eFhiMsawqaCcjzfu4+kvtnPLuUPpkRzPwO6JTBndB4BNBWX8Y3EuJ/TvyrkjezOqXyqfby7EYQzpqQkM792F5PgYluUeoLbBycmDurOxoIy4mCjueXcDOUWHeeOnE5k0tGebxb11fzkPfbiFPqkJfJVT7G7nfejSE7jm1MFtdp5Q8f0QnDdnIqdmtt31Uh2XJug25HQabn1zLU6n4eFLx9DdboZwOg0XP/01G/eW88OTB5CZlszPzxna6e+m7yg+zHl//ZILT+jDs9e2TZv1stwDXP3CCq+y6CjhlnOG8qsLRnTIa+pK0P27JfLN3eeFORoVSbQNug088dn3PGWv3+by8cb9/PGiUazLK+X9dVb/11vPG8YdU0YGeolOyTWy8eON+8m4+0P+9ePxnDMyjV++sY5ThvSgV5c4quscXDKuv1/Pio17y/jP+gKuPGUgZdX17Cyu5GBlLc8vtXpS3HT2UCYN7cm4Qd2Ij4nq0PNovKGXCRIAAAckSURBVPWzSbywdAd/vrx9m4JU56I16BbIP1Tl7t4VEyWMH9ydFTv9l6i65dyh3HHByE4/SMTX01/k8rjHNJaNGTuwG8N7dyGvpIrqegff5Zc1uu/Mk/rx5KzW9cxQqiPRGnQjnE7D01/ksqmgnF9PGUGUwJBeXTDGUNvg5C8Lt5EUF83TX1i9B56/LouJmT3cvTE82xWzf38+vbrEh+V9hNvPzxlKaVUd5dUN7mk/XdJT4+mZHM/mfeVU1zWwYF0BdQ5rXuxzR6YxMbMnDU5Dn9QEMtOSSUuJJz4mmrSUY/NaKuXpmKxB55VU8at568jefajFxwTqqfDFtiLSusS7h10ra2Tdkm1FjO7XlUEBZu2rbXBQU+8kPiZKZ+ZTCq1Bu63LK+Wedzd4dXc6fVhPBGHngUpG90tFBHKKDrOjuBKAn5yewSUn9WdsgCWqzvWYaF9ZEuOiuXBM40PM42OiO3RbslKhFDEJWkSmAU8C0cALxphH2/L1jTE8+MFmNu8rZ1CPJK4/LYNR/VKZGKCrkzGGeavyOHNEGv27NT88Wyml2kNENHGISDTwPXABkA+sAq4yxmxu7JjWNHEUltcQEyX0PEbbipVSkacjNHFMAHKNMTsARORNYCbQaIJujfTUjr2ChlLq2BIpc0z2Bzxv/+fbZV5EZI6IZItIdnFxcciCU0qpcIiUBB2o47Bf24sx5jljTJYxJistLS3AIUop1XlESoLOBwZ6PB4A+C9NoZRSx5BIuUkYg3WTcDKwF+sm4dXGmMALyVnHFAO7W3G6XsCB1sTZzjSu4EVqbBpXcCI1LghNbIONMQGbBCLiJqExpkFEfgF8itXN7t9NJWf7mFa1cYhIdmN3TMNJ4wpepMamcQUnUuOC8McWEQkawBjzEfBRuONQSqlIESlt0EoppXwciwn6uXAH0AiNK3iRGpvGFZxIjQvCHFtE3CRUSinl71isQSulVIegCVoppSLUMZOgRWSaiGwTkVwRuTvE5x4oIl+IyBYR2SQiv7TL7xORvSKyzv6Z7nHMXDvWbSIytZ3j2yUiG+wYsu2yHiLymYjk2L+7hzI2ERnpcV3WiUi5iNwejmsmIv8WkSIR2ehRFvT1EZHx9nXOFZGn5CgXV2wkrsdFZKuIfCci74lIN7s8Q0SqPa7bP9srriZiC/pvF6JrNs8jpl0iss4uD+k1C8gY0+l/sPpWbwcygThgPTAqhOfvC5xsb6dgDcoZBdwH3Blg/1F2jPHAEDv26HaMbxfQy6fsz8Dd9vbdwGPhiM3j77cfGByOawacBZwMbDya6wOsBCZhTW3wMXBhO8Q1BYixtx/ziCvDcz+f12nTuJqILei/XSiumc/zfwXuDcc1C/RzrNSg3bPlGWPqANdseSFhjNlnjFljb1cAWwgwGZSHmcCbxphaY8xOIBfrPYTSTOAVe/sV4JIwxjYZ2G6MaWrkaLvFZYz5CvBdhDKo6yMifYFUY8xyY/0Pf9XjmDaLyxiz0BjTYD/8FmvahEa1R1yNxdaEsF4zF7sWfCXwRlOv0V7XLJBjJUG3aLa8UBCRDGAcsMIu+oX9dfTfHl+TQx2vARaKyGoRmWOXpRtj9oH1AQO4lo8Jx7Wchfd/mki4ZsFen/72dqjiA7gBq3bnMkRE1orIlyJypl0W6riC+duFOrYzgUJjTI5HWViv2bGSoFs0W167ByHSBXgHuN0YUw48CwwFTgL2YX29gtDHe7ox5mTgQuAWETmriX1DGpuIxAEXA2/bRZFyzRrTWByhvm6/AxqA1+2ifcAgY8w44NfA/4pIaojjCvZvF+q/6VV4VwTCfs2OlQQd9tnyRCQWKzm/box5F8AYU2iMcRhjnMDzHPlKHtJ4jTEF9u8i4D07jkL7q5zrK11ROGLD+tBYY4wptGOMiGtG8NcnH+/mhnaLT0RmAz8ArrG/gmM3Hxy0t1djtfOOCGVcrfjbhfKaxQCXAfM84g37NTtWEvQqYLiIDLFrZLOABaE6ud229SKwxRjzhEe55+qqlwKuO8sLgFkiEi8iQ4DhWDcl2iO2ZBFJcW1j3WTaaMcw295tNvB+qGOzedVqIuGaeZyvxdfHbgapEJGJ9r+H6zyOaTNire35W+BiY0yVR3maWEvLISKZdlw7QhWXfd6g/nahjA04H9hqjHE3XUTCNWvzu46R+gNMx+o9sR34XYjPfQbWV6DvgHX2z3TgNWCDXb4A6OtxzO/sWLfRTneI7fNkYt1BXw9scl0boCewCMixf/cIQ2xJwEGgq0dZyK8Z1gfEPqAeq/Z0Y2uuD5CFlZS2A//AHsnbxnHlYrXnuv6d/dPe94f233c9sAa4qL3iaiK2oP92obhmdvnLwE0++4b0mgX60aHeSikVoY6VJg6llOpwNEErpVSE0gStlFIRShO0UkpFKE3QSikVoTRBK6VUhNIErZRSEer/AW+oqTYwi9k/AAAAAElFTkSuQmCC\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": {
        "id": "dz5o59VWxhle",
        "outputId": "f4c9f5c8-c4bf-4d35-ea91-cc5993a81ad9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x22d4fba53a0>"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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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_et['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>2022-05-02</td>\n",
              "      <td>38529.328125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1822</th>\n",
              "      <td>2022-05-03</td>\n",
              "      <td>37750.453125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1823</th>\n",
              "      <td>2022-05-04</td>\n",
              "      <td>39698.371094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1824</th>\n",
              "      <td>2022-05-05</td>\n",
              "      <td>36575.140625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1825</th>\n",
              "      <td>2022-05-06</td>\n",
              "      <td>36281.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             ds             y\n",
              "1821 2022-05-02  38529.328125\n",
              "1822 2022-05-03  37750.453125\n",
              "1823 2022-05-04  39698.371094\n",
              "1824 2022-05-05  36575.140625\n",
              "1825 2022-05-06  36281.000000"
            ]
          },
          "execution_count": 22,
          "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"
      },
      "outputs": [],
      "source": [
        "m = Prophet(interval_width=0.95,daily_seasonality=False)\n",
        "model = m.fit(data_btc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pU0y2AJJxhlg"
      },
      "outputs": [],
      "source": [
        "m_et = Prophet(interval_width=0.95,daily_seasonality=False)\n",
        "model_et = m_et.fit(data_et)"
      ]
    },
    {
      "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": [
        {
          "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>trend</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "      <th>trend_lower</th>\n",
              "      <th>trend_upper</th>\n",
              "      <th>additive_terms</th>\n",
              "      <th>additive_terms_lower</th>\n",
              "      <th>additive_terms_upper</th>\n",
              "      <th>weekly</th>\n",
              "      <th>weekly_lower</th>\n",
              "      <th>weekly_upper</th>\n",
              "      <th>yearly</th>\n",
              "      <th>yearly_lower</th>\n",
              "      <th>yearly_upper</th>\n",
              "      <th>multiplicative_terms</th>\n",
              "      <th>multiplicative_terms_lower</th>\n",
              "      <th>multiplicative_terms_upper</th>\n",
              "      <th>yhat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1921</th>\n",
              "      <td>2022-08-10</td>\n",
              "      <td>41740.648805</td>\n",
              "      <td>31948.350347</td>\n",
              "      <td>51496.515537</td>\n",
              "      <td>36114.816659</td>\n",
              "      <td>46259.189516</td>\n",
              "      <td>-48.908085</td>\n",
              "      <td>-48.908085</td>\n",
              "      <td>-48.908085</td>\n",
              "      <td>49.754693</td>\n",
              "      <td>49.754693</td>\n",
              "      <td>49.754693</td>\n",
              "      <td>-98.662778</td>\n",
              "      <td>-98.662778</td>\n",
              "      <td>-98.662778</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41691.740720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1922</th>\n",
              "      <td>2022-08-11</td>\n",
              "      <td>41726.505293</td>\n",
              "      <td>32352.332799</td>\n",
              "      <td>51798.337238</td>\n",
              "      <td>36049.753996</td>\n",
              "      <td>46296.690343</td>\n",
              "      <td>-47.270918</td>\n",
              "      <td>-47.270918</td>\n",
              "      <td>-47.270918</td>\n",
              "      <td>-49.906028</td>\n",
              "      <td>-49.906028</td>\n",
              "      <td>-49.906028</td>\n",
              "      <td>2.635110</td>\n",
              "      <td>2.635110</td>\n",
              "      <td>2.635110</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41679.234375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1923</th>\n",
              "      <td>2022-08-12</td>\n",
              "      <td>41712.361781</td>\n",
              "      <td>32688.981670</td>\n",
              "      <td>51485.925106</td>\n",
              "      <td>35984.794290</td>\n",
              "      <td>46334.191170</td>\n",
              "      <td>70.209958</td>\n",
              "      <td>70.209958</td>\n",
              "      <td>70.209958</td>\n",
              "      <td>-28.979112</td>\n",
              "      <td>-28.979112</td>\n",
              "      <td>-28.979112</td>\n",
              "      <td>99.189070</td>\n",
              "      <td>99.189070</td>\n",
              "      <td>99.189070</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41782.571739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1924</th>\n",
              "      <td>2022-08-13</td>\n",
              "      <td>41698.218270</td>\n",
              "      <td>31580.187507</td>\n",
              "      <td>50360.144382</td>\n",
              "      <td>35919.834584</td>\n",
              "      <td>46371.691997</td>\n",
              "      <td>212.910006</td>\n",
              "      <td>212.910006</td>\n",
              "      <td>212.910006</td>\n",
              "      <td>22.073053</td>\n",
              "      <td>22.073053</td>\n",
              "      <td>22.073053</td>\n",
              "      <td>190.836953</td>\n",
              "      <td>190.836953</td>\n",
              "      <td>190.836953</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41911.128276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1925</th>\n",
              "      <td>2022-08-14</td>\n",
              "      <td>41684.074758</td>\n",
              "      <td>31937.345821</td>\n",
              "      <td>51995.279241</td>\n",
              "      <td>35828.286570</td>\n",
              "      <td>46408.371932</td>\n",
              "      <td>270.906321</td>\n",
              "      <td>270.906321</td>\n",
              "      <td>270.906321</td>\n",
              "      <td>-6.511852</td>\n",
              "      <td>-6.511852</td>\n",
              "      <td>-6.511852</td>\n",
              "      <td>277.418173</td>\n",
              "      <td>277.418173</td>\n",
              "      <td>277.418173</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41954.981079</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             ds         trend    yhat_lower    yhat_upper   trend_lower  \\\n",
              "1921 2022-08-10  41740.648805  31948.350347  51496.515537  36114.816659   \n",
              "1922 2022-08-11  41726.505293  32352.332799  51798.337238  36049.753996   \n",
              "1923 2022-08-12  41712.361781  32688.981670  51485.925106  35984.794290   \n",
              "1924 2022-08-13  41698.218270  31580.187507  50360.144382  35919.834584   \n",
              "1925 2022-08-14  41684.074758  31937.345821  51995.279241  35828.286570   \n",
              "\n",
              "       trend_upper  additive_terms  additive_terms_lower  \\\n",
              "1921  46259.189516      -48.908085            -48.908085   \n",
              "1922  46296.690343      -47.270918            -47.270918   \n",
              "1923  46334.191170       70.209958             70.209958   \n",
              "1924  46371.691997      212.910006            212.910006   \n",
              "1925  46408.371932      270.906321            270.906321   \n",
              "\n",
              "      additive_terms_upper     weekly  weekly_lower  weekly_upper      yearly  \\\n",
              "1921            -48.908085  49.754693     49.754693     49.754693  -98.662778   \n",
              "1922            -47.270918 -49.906028    -49.906028    -49.906028    2.635110   \n",
              "1923             70.209958 -28.979112    -28.979112    -28.979112   99.189070   \n",
              "1924            212.910006  22.073053     22.073053     22.073053  190.836953   \n",
              "1925            270.906321  -6.511852     -6.511852     -6.511852  277.418173   \n",
              "\n",
              "      yearly_lower  yearly_upper  multiplicative_terms  \\\n",
              "1921    -98.662778    -98.662778                   0.0   \n",
              "1922      2.635110      2.635110                   0.0   \n",
              "1923     99.189070     99.189070                   0.0   \n",
              "1924    190.836953    190.836953                   0.0   \n",
              "1925    277.418173    277.418173                   0.0   \n",
              "\n",
              "      multiplicative_terms_lower  multiplicative_terms_upper          yhat  \n",
              "1921                         0.0                         0.0  41691.740720  \n",
              "1922                         0.0                         0.0  41679.234375  \n",
              "1923                         0.0                         0.0  41782.571739  \n",
              "1924                         0.0                         0.0  41911.128276  \n",
              "1925                         0.0                         0.0  41954.981079  "
            ]
          },
          "execution_count": 27,
          "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": {
        "id": "E-jAcHORxhlk",
        "outputId": "49caf282-eede-42d1-e4f1-05bf2884992b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>trend</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "      <th>trend_lower</th>\n",
              "      <th>trend_upper</th>\n",
              "      <th>additive_terms</th>\n",
              "      <th>additive_terms_lower</th>\n",
              "      <th>additive_terms_upper</th>\n",
              "      <th>weekly</th>\n",
              "      <th>weekly_lower</th>\n",
              "      <th>weekly_upper</th>\n",
              "      <th>yearly</th>\n",
              "      <th>yearly_lower</th>\n",
              "      <th>yearly_upper</th>\n",
              "      <th>multiplicative_terms</th>\n",
              "      <th>multiplicative_terms_lower</th>\n",
              "      <th>multiplicative_terms_upper</th>\n",
              "      <th>yhat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1720</th>\n",
              "      <td>2022-07-26</td>\n",
              "      <td>3640.116486</td>\n",
              "      <td>2882.899816</td>\n",
              "      <td>4178.473981</td>\n",
              "      <td>3467.864983</td>\n",
              "      <td>3831.357857</td>\n",
              "      <td>-141.049163</td>\n",
              "      <td>-141.049163</td>\n",
              "      <td>-141.049163</td>\n",
              "      <td>2.296860</td>\n",
              "      <td>2.296860</td>\n",
              "      <td>2.296860</td>\n",
              "      <td>-143.346023</td>\n",
              "      <td>-143.346023</td>\n",
              "      <td>-143.346023</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3499.067323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1721</th>\n",
              "      <td>2022-07-27</td>\n",
              "      <td>3641.882027</td>\n",
              "      <td>2836.084518</td>\n",
              "      <td>4206.537165</td>\n",
              "      <td>3466.275258</td>\n",
              "      <td>3835.654078</td>\n",
              "      <td>-124.802263</td>\n",
              "      <td>-124.802263</td>\n",
              "      <td>-124.802263</td>\n",
              "      <td>6.946895</td>\n",
              "      <td>6.946895</td>\n",
              "      <td>6.946895</td>\n",
              "      <td>-131.749158</td>\n",
              "      <td>-131.749158</td>\n",
              "      <td>-131.749158</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3517.079764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1722</th>\n",
              "      <td>2022-07-28</td>\n",
              "      <td>3643.647568</td>\n",
              "      <td>2855.235983</td>\n",
              "      <td>4197.272806</td>\n",
              "      <td>3465.191493</td>\n",
              "      <td>3839.950299</td>\n",
              "      <td>-123.781945</td>\n",
              "      <td>-123.781945</td>\n",
              "      <td>-123.781945</td>\n",
              "      <td>-3.828859</td>\n",
              "      <td>-3.828859</td>\n",
              "      <td>-3.828859</td>\n",
              "      <td>-119.953086</td>\n",
              "      <td>-119.953086</td>\n",
              "      <td>-119.953086</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3519.865624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1723</th>\n",
              "      <td>2022-07-29</td>\n",
              "      <td>3645.413110</td>\n",
              "      <td>2904.667050</td>\n",
              "      <td>4183.373990</td>\n",
              "      <td>3463.506022</td>\n",
              "      <td>3844.246521</td>\n",
              "      <td>-116.518222</td>\n",
              "      <td>-116.518222</td>\n",
              "      <td>-116.518222</td>\n",
              "      <td>-8.472777</td>\n",
              "      <td>-8.472777</td>\n",
              "      <td>-8.472777</td>\n",
              "      <td>-108.045444</td>\n",
              "      <td>-108.045444</td>\n",
              "      <td>-108.045444</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3528.894888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1724</th>\n",
              "      <td>2022-07-30</td>\n",
              "      <td>3647.178651</td>\n",
              "      <td>2884.477600</td>\n",
              "      <td>4230.822578</td>\n",
              "      <td>3462.486938</td>\n",
              "      <td>3848.542742</td>\n",
              "      <td>-96.091611</td>\n",
              "      <td>-96.091611</td>\n",
              "      <td>-96.091611</td>\n",
              "      <td>0.017373</td>\n",
              "      <td>0.017373</td>\n",
              "      <td>0.017373</td>\n",
              "      <td>-96.108984</td>\n",
              "      <td>-96.108984</td>\n",
              "      <td>-96.108984</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3551.087040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1725</th>\n",
              "      <td>2022-07-31</td>\n",
              "      <td>3648.944192</td>\n",
              "      <td>2919.215854</td>\n",
              "      <td>4254.855671</td>\n",
              "      <td>3461.555210</td>\n",
              "      <td>3854.636209</td>\n",
              "      <td>-82.560859</td>\n",
              "      <td>-82.560859</td>\n",
              "      <td>-82.560859</td>\n",
              "      <td>1.659657</td>\n",
              "      <td>1.659657</td>\n",
              "      <td>1.659657</td>\n",
              "      <td>-84.220516</td>\n",
              "      <td>-84.220516</td>\n",
              "      <td>-84.220516</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3566.383334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1726</th>\n",
              "      <td>2022-08-01</td>\n",
              "      <td>3650.709734</td>\n",
              "      <td>2956.637219</td>\n",
              "      <td>4259.129197</td>\n",
              "      <td>3460.623482</td>\n",
              "      <td>3860.947880</td>\n",
              "      <td>-71.069207</td>\n",
              "      <td>-71.069207</td>\n",
              "      <td>-71.069207</td>\n",
              "      <td>1.380852</td>\n",
              "      <td>1.380852</td>\n",
              "      <td>1.380852</td>\n",
              "      <td>-72.450059</td>\n",
              "      <td>-72.450059</td>\n",
              "      <td>-72.450059</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3579.640527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1727</th>\n",
              "      <td>2022-08-02</td>\n",
              "      <td>3652.475275</td>\n",
              "      <td>2912.640799</td>\n",
              "      <td>4227.474355</td>\n",
              "      <td>3459.156593</td>\n",
              "      <td>3866.513305</td>\n",
              "      <td>-58.563330</td>\n",
              "      <td>-58.563330</td>\n",
              "      <td>-58.563330</td>\n",
              "      <td>2.296860</td>\n",
              "      <td>2.296860</td>\n",
              "      <td>2.296860</td>\n",
              "      <td>-60.860190</td>\n",
              "      <td>-60.860190</td>\n",
              "      <td>-60.860190</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3593.911945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1728</th>\n",
              "      <td>2022-08-03</td>\n",
              "      <td>3654.240816</td>\n",
              "      <td>2927.740334</td>\n",
              "      <td>4355.917171</td>\n",
              "      <td>3458.524338</td>\n",
              "      <td>3870.949627</td>\n",
              "      <td>-42.558707</td>\n",
              "      <td>-42.558707</td>\n",
              "      <td>-42.558707</td>\n",
              "      <td>6.946895</td>\n",
              "      <td>6.946895</td>\n",
              "      <td>6.946895</td>\n",
              "      <td>-49.505602</td>\n",
              "      <td>-49.505602</td>\n",
              "      <td>-49.505602</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3611.682109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1729</th>\n",
              "      <td>2022-08-04</td>\n",
              "      <td>3656.006358</td>\n",
              "      <td>2936.572700</td>\n",
              "      <td>4304.225608</td>\n",
              "      <td>3457.443646</td>\n",
              "      <td>3875.333174</td>\n",
              "      <td>-42.261730</td>\n",
              "      <td>-42.261730</td>\n",
              "      <td>-42.261730</td>\n",
              "      <td>-3.828859</td>\n",
              "      <td>-3.828859</td>\n",
              "      <td>-3.828859</td>\n",
              "      <td>-38.432871</td>\n",
              "      <td>-38.432871</td>\n",
              "      <td>-38.432871</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3613.744628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1730</th>\n",
              "      <td>2022-08-05</td>\n",
              "      <td>3657.771899</td>\n",
              "      <td>2945.305322</td>\n",
              "      <td>4281.668775</td>\n",
              "      <td>3456.356510</td>\n",
              "      <td>3880.403499</td>\n",
              "      <td>-36.153201</td>\n",
              "      <td>-36.153201</td>\n",
              "      <td>-36.153201</td>\n",
              "      <td>-8.472777</td>\n",
              "      <td>-8.472777</td>\n",
              "      <td>-8.472777</td>\n",
              "      <td>-27.680424</td>\n",
              "      <td>-27.680424</td>\n",
              "      <td>-27.680424</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3621.618698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1731</th>\n",
              "      <td>2022-08-06</td>\n",
              "      <td>3659.537440</td>\n",
              "      <td>2973.508774</td>\n",
              "      <td>4307.064637</td>\n",
              "      <td>3455.269373</td>\n",
              "      <td>3886.037769</td>\n",
              "      <td>-17.261317</td>\n",
              "      <td>-17.261317</td>\n",
              "      <td>-17.261317</td>\n",
              "      <td>0.017373</td>\n",
              "      <td>0.017373</td>\n",
              "      <td>0.017373</td>\n",
              "      <td>-17.278690</td>\n",
              "      <td>-17.278690</td>\n",
              "      <td>-17.278690</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3642.276123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1732</th>\n",
              "      <td>2022-08-07</td>\n",
              "      <td>3661.302981</td>\n",
              "      <td>2980.837650</td>\n",
              "      <td>4308.124191</td>\n",
              "      <td>3454.182236</td>\n",
              "      <td>3891.163101</td>\n",
              "      <td>-5.590781</td>\n",
              "      <td>-5.590781</td>\n",
              "      <td>-5.590781</td>\n",
              "      <td>1.659657</td>\n",
              "      <td>1.659657</td>\n",
              "      <td>1.659657</td>\n",
              "      <td>-7.250438</td>\n",
              "      <td>-7.250438</td>\n",
              "      <td>-7.250438</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3655.712201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1733</th>\n",
              "      <td>2022-08-08</td>\n",
              "      <td>3663.068523</td>\n",
              "      <td>2989.889244</td>\n",
              "      <td>4329.631658</td>\n",
              "      <td>3453.095099</td>\n",
              "      <td>3896.511847</td>\n",
              "      <td>3.769591</td>\n",
              "      <td>3.769591</td>\n",
              "      <td>3.769591</td>\n",
              "      <td>1.380852</td>\n",
              "      <td>1.380852</td>\n",
              "      <td>1.380852</td>\n",
              "      <td>2.388739</td>\n",
              "      <td>2.388739</td>\n",
              "      <td>2.388739</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3666.838114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1734</th>\n",
              "      <td>2022-08-09</td>\n",
              "      <td>3664.834064</td>\n",
              "      <td>3041.878678</td>\n",
              "      <td>4337.951326</td>\n",
              "      <td>3452.007963</td>\n",
              "      <td>3901.826332</td>\n",
              "      <td>13.926655</td>\n",
              "      <td>13.926655</td>\n",
              "      <td>13.926655</td>\n",
              "      <td>2.296860</td>\n",
              "      <td>2.296860</td>\n",
              "      <td>2.296860</td>\n",
              "      <td>11.629795</td>\n",
              "      <td>11.629795</td>\n",
              "      <td>11.629795</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3678.760719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1735</th>\n",
              "      <td>2022-08-10</td>\n",
              "      <td>3666.599605</td>\n",
              "      <td>3047.651181</td>\n",
              "      <td>4390.254586</td>\n",
              "      <td>3450.581584</td>\n",
              "      <td>3906.204446</td>\n",
              "      <td>27.416381</td>\n",
              "      <td>27.416381</td>\n",
              "      <td>27.416381</td>\n",
              "      <td>6.946895</td>\n",
              "      <td>6.946895</td>\n",
              "      <td>6.946895</td>\n",
              "      <td>20.469486</td>\n",
              "      <td>20.469486</td>\n",
              "      <td>20.469486</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3694.015986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1736</th>\n",
              "      <td>2022-08-11</td>\n",
              "      <td>3668.365147</td>\n",
              "      <td>3001.957148</td>\n",
              "      <td>4321.340933</td>\n",
              "      <td>3447.749926</td>\n",
              "      <td>3910.582561</td>\n",
              "      <td>25.080679</td>\n",
              "      <td>25.080679</td>\n",
              "      <td>25.080679</td>\n",
              "      <td>-3.828859</td>\n",
              "      <td>-3.828859</td>\n",
              "      <td>-3.828859</td>\n",
              "      <td>28.909538</td>\n",
              "      <td>28.909538</td>\n",
              "      <td>28.909538</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3693.445826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1737</th>\n",
              "      <td>2022-08-12</td>\n",
              "      <td>3670.130688</td>\n",
              "      <td>3007.233511</td>\n",
              "      <td>4339.841706</td>\n",
              "      <td>3444.918267</td>\n",
              "      <td>3915.989801</td>\n",
              "      <td>28.482965</td>\n",
              "      <td>28.482965</td>\n",
              "      <td>28.482965</td>\n",
              "      <td>-8.472777</td>\n",
              "      <td>-8.472777</td>\n",
              "      <td>-8.472777</td>\n",
              "      <td>36.955742</td>\n",
              "      <td>36.955742</td>\n",
              "      <td>36.955742</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3698.613653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1738</th>\n",
              "      <td>2022-08-13</td>\n",
              "      <td>3671.896229</td>\n",
              "      <td>3052.697782</td>\n",
              "      <td>4361.069026</td>\n",
              "      <td>3442.086608</td>\n",
              "      <td>3921.668790</td>\n",
              "      <td>44.634385</td>\n",
              "      <td>44.634385</td>\n",
              "      <td>44.634385</td>\n",
              "      <td>0.017373</td>\n",
              "      <td>0.017373</td>\n",
              "      <td>0.017373</td>\n",
              "      <td>44.617012</td>\n",
              "      <td>44.617012</td>\n",
              "      <td>44.617012</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3716.530615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1739</th>\n",
              "      <td>2022-08-14</td>\n",
              "      <td>3673.661771</td>\n",
              "      <td>3054.624672</td>\n",
              "      <td>4406.593312</td>\n",
              "      <td>3439.290907</td>\n",
              "      <td>3927.347779</td>\n",
              "      <td>53.564082</td>\n",
              "      <td>53.564082</td>\n",
              "      <td>53.564082</td>\n",
              "      <td>1.659657</td>\n",
              "      <td>1.659657</td>\n",
              "      <td>1.659657</td>\n",
              "      <td>51.904425</td>\n",
              "      <td>51.904425</td>\n",
              "      <td>51.904425</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3727.225852</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             ds        trend   yhat_lower   yhat_upper  trend_lower  \\\n",
              "1720 2022-07-26  3640.116486  2882.899816  4178.473981  3467.864983   \n",
              "1721 2022-07-27  3641.882027  2836.084518  4206.537165  3466.275258   \n",
              "1722 2022-07-28  3643.647568  2855.235983  4197.272806  3465.191493   \n",
              "1723 2022-07-29  3645.413110  2904.667050  4183.373990  3463.506022   \n",
              "1724 2022-07-30  3647.178651  2884.477600  4230.822578  3462.486938   \n",
              "1725 2022-07-31  3648.944192  2919.215854  4254.855671  3461.555210   \n",
              "1726 2022-08-01  3650.709734  2956.637219  4259.129197  3460.623482   \n",
              "1727 2022-08-02  3652.475275  2912.640799  4227.474355  3459.156593   \n",
              "1728 2022-08-03  3654.240816  2927.740334  4355.917171  3458.524338   \n",
              "1729 2022-08-04  3656.006358  2936.572700  4304.225608  3457.443646   \n",
              "1730 2022-08-05  3657.771899  2945.305322  4281.668775  3456.356510   \n",
              "1731 2022-08-06  3659.537440  2973.508774  4307.064637  3455.269373   \n",
              "1732 2022-08-07  3661.302981  2980.837650  4308.124191  3454.182236   \n",
              "1733 2022-08-08  3663.068523  2989.889244  4329.631658  3453.095099   \n",
              "1734 2022-08-09  3664.834064  3041.878678  4337.951326  3452.007963   \n",
              "1735 2022-08-10  3666.599605  3047.651181  4390.254586  3450.581584   \n",
              "1736 2022-08-11  3668.365147  3001.957148  4321.340933  3447.749926   \n",
              "1737 2022-08-12  3670.130688  3007.233511  4339.841706  3444.918267   \n",
              "1738 2022-08-13  3671.896229  3052.697782  4361.069026  3442.086608   \n",
              "1739 2022-08-14  3673.661771  3054.624672  4406.593312  3439.290907   \n",
              "\n",
              "      trend_upper  additive_terms  additive_terms_lower  additive_terms_upper  \\\n",
              "1720  3831.357857     -141.049163           -141.049163           -141.049163   \n",
              "1721  3835.654078     -124.802263           -124.802263           -124.802263   \n",
              "1722  3839.950299     -123.781945           -123.781945           -123.781945   \n",
              "1723  3844.246521     -116.518222           -116.518222           -116.518222   \n",
              "1724  3848.542742      -96.091611            -96.091611            -96.091611   \n",
              "1725  3854.636209      -82.560859            -82.560859            -82.560859   \n",
              "1726  3860.947880      -71.069207            -71.069207            -71.069207   \n",
              "1727  3866.513305      -58.563330            -58.563330            -58.563330   \n",
              "1728  3870.949627      -42.558707            -42.558707            -42.558707   \n",
              "1729  3875.333174      -42.261730            -42.261730            -42.261730   \n",
              "1730  3880.403499      -36.153201            -36.153201            -36.153201   \n",
              "1731  3886.037769      -17.261317            -17.261317            -17.261317   \n",
              "1732  3891.163101       -5.590781             -5.590781             -5.590781   \n",
              "1733  3896.511847        3.769591              3.769591              3.769591   \n",
              "1734  3901.826332       13.926655             13.926655             13.926655   \n",
              "1735  3906.204446       27.416381             27.416381             27.416381   \n",
              "1736  3910.582561       25.080679             25.080679             25.080679   \n",
              "1737  3915.989801       28.482965             28.482965             28.482965   \n",
              "1738  3921.668790       44.634385             44.634385             44.634385   \n",
              "1739  3927.347779       53.564082             53.564082             53.564082   \n",
              "\n",
              "        weekly  weekly_lower  weekly_upper      yearly  yearly_lower  \\\n",
              "1720  2.296860      2.296860      2.296860 -143.346023   -143.346023   \n",
              "1721  6.946895      6.946895      6.946895 -131.749158   -131.749158   \n",
              "1722 -3.828859     -3.828859     -3.828859 -119.953086   -119.953086   \n",
              "1723 -8.472777     -8.472777     -8.472777 -108.045444   -108.045444   \n",
              "1724  0.017373      0.017373      0.017373  -96.108984    -96.108984   \n",
              "1725  1.659657      1.659657      1.659657  -84.220516    -84.220516   \n",
              "1726  1.380852      1.380852      1.380852  -72.450059    -72.450059   \n",
              "1727  2.296860      2.296860      2.296860  -60.860190    -60.860190   \n",
              "1728  6.946895      6.946895      6.946895  -49.505602    -49.505602   \n",
              "1729 -3.828859     -3.828859     -3.828859  -38.432871    -38.432871   \n",
              "1730 -8.472777     -8.472777     -8.472777  -27.680424    -27.680424   \n",
              "1731  0.017373      0.017373      0.017373  -17.278690    -17.278690   \n",
              "1732  1.659657      1.659657      1.659657   -7.250438     -7.250438   \n",
              "1733  1.380852      1.380852      1.380852    2.388739      2.388739   \n",
              "1734  2.296860      2.296860      2.296860   11.629795     11.629795   \n",
              "1735  6.946895      6.946895      6.946895   20.469486     20.469486   \n",
              "1736 -3.828859     -3.828859     -3.828859   28.909538     28.909538   \n",
              "1737 -8.472777     -8.472777     -8.472777   36.955742     36.955742   \n",
              "1738  0.017373      0.017373      0.017373   44.617012     44.617012   \n",
              "1739  1.659657      1.659657      1.659657   51.904425     51.904425   \n",
              "\n",
              "      yearly_upper  multiplicative_terms  multiplicative_terms_lower  \\\n",
              "1720   -143.346023                   0.0                         0.0   \n",
              "1721   -131.749158                   0.0                         0.0   \n",
              "1722   -119.953086                   0.0                         0.0   \n",
              "1723   -108.045444                   0.0                         0.0   \n",
              "1724    -96.108984                   0.0                         0.0   \n",
              "1725    -84.220516                   0.0                         0.0   \n",
              "1726    -72.450059                   0.0                         0.0   \n",
              "1727    -60.860190                   0.0                         0.0   \n",
              "1728    -49.505602                   0.0                         0.0   \n",
              "1729    -38.432871                   0.0                         0.0   \n",
              "1730    -27.680424                   0.0                         0.0   \n",
              "1731    -17.278690                   0.0                         0.0   \n",
              "1732     -7.250438                   0.0                         0.0   \n",
              "1733      2.388739                   0.0                         0.0   \n",
              "1734     11.629795                   0.0                         0.0   \n",
              "1735     20.469486                   0.0                         0.0   \n",
              "1736     28.909538                   0.0                         0.0   \n",
              "1737     36.955742                   0.0                         0.0   \n",
              "1738     44.617012                   0.0                         0.0   \n",
              "1739     51.904425                   0.0                         0.0   \n",
              "\n",
              "      multiplicative_terms_upper         yhat  \n",
              "1720                         0.0  3499.067323  \n",
              "1721                         0.0  3517.079764  \n",
              "1722                         0.0  3519.865624  \n",
              "1723                         0.0  3528.894888  \n",
              "1724                         0.0  3551.087040  \n",
              "1725                         0.0  3566.383334  \n",
              "1726                         0.0  3579.640527  \n",
              "1727                         0.0  3593.911945  \n",
              "1728                         0.0  3611.682109  \n",
              "1729                         0.0  3613.744628  \n",
              "1730                         0.0  3621.618698  \n",
              "1731                         0.0  3642.276123  \n",
              "1732                         0.0  3655.712201  \n",
              "1733                         0.0  3666.838114  \n",
              "1734                         0.0  3678.760719  \n",
              "1735                         0.0  3694.015986  \n",
              "1736                         0.0  3693.445826  \n",
              "1737                         0.0  3698.613653  \n",
              "1738                         0.0  3716.530615  \n",
              "1739                         0.0  3727.225852  "
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "future = m_et.make_future_dataframe(periods=100, freq='D')\n",
        "forecast_et = m_et.predict(future)\n",
        "forecast_et.tail(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "LJqRB-UMsMQw",
        "outputId": "22f2925e-c06c-424d-a435-2d3250b650d6"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>trend</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "      <th>trend_lower</th>\n",
              "      <th>trend_upper</th>\n",
              "      <th>additive_terms</th>\n",
              "      <th>additive_terms_lower</th>\n",
              "      <th>additive_terms_upper</th>\n",
              "      <th>weekly</th>\n",
              "      <th>weekly_lower</th>\n",
              "      <th>weekly_upper</th>\n",
              "      <th>yearly</th>\n",
              "      <th>yearly_lower</th>\n",
              "      <th>yearly_upper</th>\n",
              "      <th>multiplicative_terms</th>\n",
              "      <th>multiplicative_terms_lower</th>\n",
              "      <th>multiplicative_terms_upper</th>\n",
              "      <th>yhat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1906</th>\n",
              "      <td>2022-07-26</td>\n",
              "      <td>41952.801482</td>\n",
              "      <td>30341.829091</td>\n",
              "      <td>48511.259687</td>\n",
              "      <td>37626.381965</td>\n",
              "      <td>45487.039890</td>\n",
              "      <td>-2006.517010</td>\n",
              "      <td>-2006.517010</td>\n",
              "      <td>-2006.517010</td>\n",
              "      <td>-11.462046</td>\n",
              "      <td>-11.462046</td>\n",
              "      <td>-11.462046</td>\n",
              "      <td>-1995.054965</td>\n",
              "      <td>-1995.054965</td>\n",
              "      <td>-1995.054965</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39946.284471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1907</th>\n",
              "      <td>2022-07-27</td>\n",
              "      <td>41938.657970</td>\n",
              "      <td>30694.398600</td>\n",
              "      <td>50181.951143</td>\n",
              "      <td>37555.852627</td>\n",
              "      <td>45526.928643</td>\n",
              "      <td>-1813.326657</td>\n",
              "      <td>-1813.326657</td>\n",
              "      <td>-1813.326657</td>\n",
              "      <td>49.754693</td>\n",
              "      <td>49.754693</td>\n",
              "      <td>49.754693</td>\n",
              "      <td>-1863.081349</td>\n",
              "      <td>-1863.081349</td>\n",
              "      <td>-1863.081349</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40125.331313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1908</th>\n",
              "      <td>2022-07-28</td>\n",
              "      <td>41924.514458</td>\n",
              "      <td>30548.535300</td>\n",
              "      <td>49035.203180</td>\n",
              "      <td>37485.323289</td>\n",
              "      <td>45565.961036</td>\n",
              "      <td>-1778.901800</td>\n",
              "      <td>-1778.901800</td>\n",
              "      <td>-1778.901800</td>\n",
              "      <td>-49.906028</td>\n",
              "      <td>-49.906028</td>\n",
              "      <td>-49.906028</td>\n",
              "      <td>-1728.995772</td>\n",
              "      <td>-1728.995772</td>\n",
              "      <td>-1728.995772</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40145.612658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1909</th>\n",
              "      <td>2022-07-29</td>\n",
              "      <td>41910.370946</td>\n",
              "      <td>30914.316232</td>\n",
              "      <td>50169.384821</td>\n",
              "      <td>37398.520158</td>\n",
              "      <td>45604.993428</td>\n",
              "      <td>-1622.622290</td>\n",
              "      <td>-1622.622290</td>\n",
              "      <td>-1622.622290</td>\n",
              "      <td>-28.979112</td>\n",
              "      <td>-28.979112</td>\n",
              "      <td>-28.979112</td>\n",
              "      <td>-1593.643178</td>\n",
              "      <td>-1593.643178</td>\n",
              "      <td>-1593.643178</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40287.748656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1910</th>\n",
              "      <td>2022-07-30</td>\n",
              "      <td>41896.227435</td>\n",
              "      <td>31616.777716</td>\n",
              "      <td>49059.152782</td>\n",
              "      <td>37266.932571</td>\n",
              "      <td>45644.025821</td>\n",
              "      <td>-1435.743998</td>\n",
              "      <td>-1435.743998</td>\n",
              "      <td>-1435.743998</td>\n",
              "      <td>22.073053</td>\n",
              "      <td>22.073053</td>\n",
              "      <td>22.073053</td>\n",
              "      <td>-1457.817052</td>\n",
              "      <td>-1457.817052</td>\n",
              "      <td>-1457.817052</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40460.483436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1911</th>\n",
              "      <td>2022-07-31</td>\n",
              "      <td>41882.083923</td>\n",
              "      <td>31090.784124</td>\n",
              "      <td>50204.164350</td>\n",
              "      <td>37185.268544</td>\n",
              "      <td>45683.058214</td>\n",
              "      <td>-1328.764949</td>\n",
              "      <td>-1328.764949</td>\n",
              "      <td>-1328.764949</td>\n",
              "      <td>-6.511852</td>\n",
              "      <td>-6.511852</td>\n",
              "      <td>-6.511852</td>\n",
              "      <td>-1322.253096</td>\n",
              "      <td>-1322.253096</td>\n",
              "      <td>-1322.253096</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40553.318974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1912</th>\n",
              "      <td>2022-08-01</td>\n",
              "      <td>41867.940411</td>\n",
              "      <td>31041.991635</td>\n",
              "      <td>49463.813071</td>\n",
              "      <td>37098.682765</td>\n",
              "      <td>45752.802036</td>\n",
              "      <td>-1162.593694</td>\n",
              "      <td>-1162.593694</td>\n",
              "      <td>-1162.593694</td>\n",
              "      <td>25.031293</td>\n",
              "      <td>25.031293</td>\n",
              "      <td>25.031293</td>\n",
              "      <td>-1187.624987</td>\n",
              "      <td>-1187.624987</td>\n",
              "      <td>-1187.624987</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40705.346717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1913</th>\n",
              "      <td>2022-08-02</td>\n",
              "      <td>41853.796899</td>\n",
              "      <td>31257.197166</td>\n",
              "      <td>50435.084781</td>\n",
              "      <td>36970.646707</td>\n",
              "      <td>45823.428136</td>\n",
              "      <td>-1066.004188</td>\n",
              "      <td>-1066.004188</td>\n",
              "      <td>-1066.004188</td>\n",
              "      <td>-11.462046</td>\n",
              "      <td>-11.462046</td>\n",
              "      <td>-11.462046</td>\n",
              "      <td>-1054.542142</td>\n",
              "      <td>-1054.542142</td>\n",
              "      <td>-1054.542142</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40787.792712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1914</th>\n",
              "      <td>2022-08-03</td>\n",
              "      <td>41839.653388</td>\n",
              "      <td>32157.913114</td>\n",
              "      <td>50415.182399</td>\n",
              "      <td>36864.438523</td>\n",
              "      <td>45894.054236</td>\n",
              "      <td>-873.794715</td>\n",
              "      <td>-873.794715</td>\n",
              "      <td>-873.794715</td>\n",
              "      <td>49.754693</td>\n",
              "      <td>49.754693</td>\n",
              "      <td>49.754693</td>\n",
              "      <td>-923.549408</td>\n",
              "      <td>-923.549408</td>\n",
              "      <td>-923.549408</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40965.858672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1915</th>\n",
              "      <td>2022-08-04</td>\n",
              "      <td>41825.509876</td>\n",
              "      <td>30958.753927</td>\n",
              "      <td>50679.130653</td>\n",
              "      <td>36762.115712</td>\n",
              "      <td>45964.680337</td>\n",
              "      <td>-845.034550</td>\n",
              "      <td>-845.034550</td>\n",
              "      <td>-845.034550</td>\n",
              "      <td>-49.906028</td>\n",
              "      <td>-49.906028</td>\n",
              "      <td>-49.906028</td>\n",
              "      <td>-795.128522</td>\n",
              "      <td>-795.128522</td>\n",
              "      <td>-795.128522</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40980.475326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1916</th>\n",
              "      <td>2022-08-05</td>\n",
              "      <td>41811.366364</td>\n",
              "      <td>31940.465737</td>\n",
              "      <td>50469.258915</td>\n",
              "      <td>36644.040880</td>\n",
              "      <td>46017.594242</td>\n",
              "      <td>-698.680263</td>\n",
              "      <td>-698.680263</td>\n",
              "      <td>-698.680263</td>\n",
              "      <td>-28.979112</td>\n",
              "      <td>-28.979112</td>\n",
              "      <td>-28.979112</td>\n",
              "      <td>-669.701150</td>\n",
              "      <td>-669.701150</td>\n",
              "      <td>-669.701150</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41112.686101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1917</th>\n",
              "      <td>2022-08-06</td>\n",
              "      <td>41797.222852</td>\n",
              "      <td>31053.549525</td>\n",
              "      <td>50214.794171</td>\n",
              "      <td>36525.966048</td>\n",
              "      <td>46059.226031</td>\n",
              "      <td>-525.560255</td>\n",
              "      <td>-525.560255</td>\n",
              "      <td>-525.560255</td>\n",
              "      <td>22.073053</td>\n",
              "      <td>22.073053</td>\n",
              "      <td>22.073053</td>\n",
              "      <td>-547.633308</td>\n",
              "      <td>-547.633308</td>\n",
              "      <td>-547.633308</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41271.662597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1918</th>\n",
              "      <td>2022-08-07</td>\n",
              "      <td>41783.079340</td>\n",
              "      <td>31237.851338</td>\n",
              "      <td>50603.047214</td>\n",
              "      <td>36407.891216</td>\n",
              "      <td>46129.451455</td>\n",
              "      <td>-435.752759</td>\n",
              "      <td>-435.752759</td>\n",
              "      <td>-435.752759</td>\n",
              "      <td>-6.511852</td>\n",
              "      <td>-6.511852</td>\n",
              "      <td>-6.511852</td>\n",
              "      <td>-429.240907</td>\n",
              "      <td>-429.240907</td>\n",
              "      <td>-429.240907</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41347.326581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1919</th>\n",
              "      <td>2022-08-08</td>\n",
              "      <td>41768.935829</td>\n",
              "      <td>31745.456853</td>\n",
              "      <td>50924.688599</td>\n",
              "      <td>36291.017325</td>\n",
              "      <td>46183.473765</td>\n",
              "      <td>-289.764882</td>\n",
              "      <td>-289.764882</td>\n",
              "      <td>-289.764882</td>\n",
              "      <td>25.031293</td>\n",
              "      <td>25.031293</td>\n",
              "      <td>25.031293</td>\n",
              "      <td>-314.796175</td>\n",
              "      <td>-314.796175</td>\n",
              "      <td>-314.796175</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41479.170947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1920</th>\n",
              "      <td>2022-08-09</td>\n",
              "      <td>41754.792317</td>\n",
              "      <td>32398.961524</td>\n",
              "      <td>51104.487359</td>\n",
              "      <td>36180.625473</td>\n",
              "      <td>46221.498502</td>\n",
              "      <td>-215.996743</td>\n",
              "      <td>-215.996743</td>\n",
              "      <td>-215.996743</td>\n",
              "      <td>-11.462046</td>\n",
              "      <td>-11.462046</td>\n",
              "      <td>-11.462046</td>\n",
              "      <td>-204.534697</td>\n",
              "      <td>-204.534697</td>\n",
              "      <td>-204.534697</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41538.795574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1921</th>\n",
              "      <td>2022-08-10</td>\n",
              "      <td>41740.648805</td>\n",
              "      <td>31948.350347</td>\n",
              "      <td>51496.515537</td>\n",
              "      <td>36114.816659</td>\n",
              "      <td>46259.189516</td>\n",
              "      <td>-48.908085</td>\n",
              "      <td>-48.908085</td>\n",
              "      <td>-48.908085</td>\n",
              "      <td>49.754693</td>\n",
              "      <td>49.754693</td>\n",
              "      <td>49.754693</td>\n",
              "      <td>-98.662778</td>\n",
              "      <td>-98.662778</td>\n",
              "      <td>-98.662778</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41691.740720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1922</th>\n",
              "      <td>2022-08-11</td>\n",
              "      <td>41726.505293</td>\n",
              "      <td>32352.332799</td>\n",
              "      <td>51798.337238</td>\n",
              "      <td>36049.753996</td>\n",
              "      <td>46296.690343</td>\n",
              "      <td>-47.270918</td>\n",
              "      <td>-47.270918</td>\n",
              "      <td>-47.270918</td>\n",
              "      <td>-49.906028</td>\n",
              "      <td>-49.906028</td>\n",
              "      <td>-49.906028</td>\n",
              "      <td>2.635110</td>\n",
              "      <td>2.635110</td>\n",
              "      <td>2.635110</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41679.234375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1923</th>\n",
              "      <td>2022-08-12</td>\n",
              "      <td>41712.361781</td>\n",
              "      <td>32688.981670</td>\n",
              "      <td>51485.925106</td>\n",
              "      <td>35984.794290</td>\n",
              "      <td>46334.191170</td>\n",
              "      <td>70.209958</td>\n",
              "      <td>70.209958</td>\n",
              "      <td>70.209958</td>\n",
              "      <td>-28.979112</td>\n",
              "      <td>-28.979112</td>\n",
              "      <td>-28.979112</td>\n",
              "      <td>99.189070</td>\n",
              "      <td>99.189070</td>\n",
              "      <td>99.189070</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41782.571739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1924</th>\n",
              "      <td>2022-08-13</td>\n",
              "      <td>41698.218270</td>\n",
              "      <td>31580.187507</td>\n",
              "      <td>50360.144382</td>\n",
              "      <td>35919.834584</td>\n",
              "      <td>46371.691997</td>\n",
              "      <td>212.910006</td>\n",
              "      <td>212.910006</td>\n",
              "      <td>212.910006</td>\n",
              "      <td>22.073053</td>\n",
              "      <td>22.073053</td>\n",
              "      <td>22.073053</td>\n",
              "      <td>190.836953</td>\n",
              "      <td>190.836953</td>\n",
              "      <td>190.836953</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41911.128276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1925</th>\n",
              "      <td>2022-08-14</td>\n",
              "      <td>41684.074758</td>\n",
              "      <td>31937.345821</td>\n",
              "      <td>51995.279241</td>\n",
              "      <td>35828.286570</td>\n",
              "      <td>46408.371932</td>\n",
              "      <td>270.906321</td>\n",
              "      <td>270.906321</td>\n",
              "      <td>270.906321</td>\n",
              "      <td>-6.511852</td>\n",
              "      <td>-6.511852</td>\n",
              "      <td>-6.511852</td>\n",
              "      <td>277.418173</td>\n",
              "      <td>277.418173</td>\n",
              "      <td>277.418173</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41954.981079</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             ds         trend    yhat_lower    yhat_upper   trend_lower  \\\n",
              "1906 2022-07-26  41952.801482  30341.829091  48511.259687  37626.381965   \n",
              "1907 2022-07-27  41938.657970  30694.398600  50181.951143  37555.852627   \n",
              "1908 2022-07-28  41924.514458  30548.535300  49035.203180  37485.323289   \n",
              "1909 2022-07-29  41910.370946  30914.316232  50169.384821  37398.520158   \n",
              "1910 2022-07-30  41896.227435  31616.777716  49059.152782  37266.932571   \n",
              "1911 2022-07-31  41882.083923  31090.784124  50204.164350  37185.268544   \n",
              "1912 2022-08-01  41867.940411  31041.991635  49463.813071  37098.682765   \n",
              "1913 2022-08-02  41853.796899  31257.197166  50435.084781  36970.646707   \n",
              "1914 2022-08-03  41839.653388  32157.913114  50415.182399  36864.438523   \n",
              "1915 2022-08-04  41825.509876  30958.753927  50679.130653  36762.115712   \n",
              "1916 2022-08-05  41811.366364  31940.465737  50469.258915  36644.040880   \n",
              "1917 2022-08-06  41797.222852  31053.549525  50214.794171  36525.966048   \n",
              "1918 2022-08-07  41783.079340  31237.851338  50603.047214  36407.891216   \n",
              "1919 2022-08-08  41768.935829  31745.456853  50924.688599  36291.017325   \n",
              "1920 2022-08-09  41754.792317  32398.961524  51104.487359  36180.625473   \n",
              "1921 2022-08-10  41740.648805  31948.350347  51496.515537  36114.816659   \n",
              "1922 2022-08-11  41726.505293  32352.332799  51798.337238  36049.753996   \n",
              "1923 2022-08-12  41712.361781  32688.981670  51485.925106  35984.794290   \n",
              "1924 2022-08-13  41698.218270  31580.187507  50360.144382  35919.834584   \n",
              "1925 2022-08-14  41684.074758  31937.345821  51995.279241  35828.286570   \n",
              "\n",
              "       trend_upper  additive_terms  additive_terms_lower  \\\n",
              "1906  45487.039890    -2006.517010          -2006.517010   \n",
              "1907  45526.928643    -1813.326657          -1813.326657   \n",
              "1908  45565.961036    -1778.901800          -1778.901800   \n",
              "1909  45604.993428    -1622.622290          -1622.622290   \n",
              "1910  45644.025821    -1435.743998          -1435.743998   \n",
              "1911  45683.058214    -1328.764949          -1328.764949   \n",
              "1912  45752.802036    -1162.593694          -1162.593694   \n",
              "1913  45823.428136    -1066.004188          -1066.004188   \n",
              "1914  45894.054236     -873.794715           -873.794715   \n",
              "1915  45964.680337     -845.034550           -845.034550   \n",
              "1916  46017.594242     -698.680263           -698.680263   \n",
              "1917  46059.226031     -525.560255           -525.560255   \n",
              "1918  46129.451455     -435.752759           -435.752759   \n",
              "1919  46183.473765     -289.764882           -289.764882   \n",
              "1920  46221.498502     -215.996743           -215.996743   \n",
              "1921  46259.189516      -48.908085            -48.908085   \n",
              "1922  46296.690343      -47.270918            -47.270918   \n",
              "1923  46334.191170       70.209958             70.209958   \n",
              "1924  46371.691997      212.910006            212.910006   \n",
              "1925  46408.371932      270.906321            270.906321   \n",
              "\n",
              "      additive_terms_upper     weekly  weekly_lower  weekly_upper  \\\n",
              "1906          -2006.517010 -11.462046    -11.462046    -11.462046   \n",
              "1907          -1813.326657  49.754693     49.754693     49.754693   \n",
              "1908          -1778.901800 -49.906028    -49.906028    -49.906028   \n",
              "1909          -1622.622290 -28.979112    -28.979112    -28.979112   \n",
              "1910          -1435.743998  22.073053     22.073053     22.073053   \n",
              "1911          -1328.764949  -6.511852     -6.511852     -6.511852   \n",
              "1912          -1162.593694  25.031293     25.031293     25.031293   \n",
              "1913          -1066.004188 -11.462046    -11.462046    -11.462046   \n",
              "1914           -873.794715  49.754693     49.754693     49.754693   \n",
              "1915           -845.034550 -49.906028    -49.906028    -49.906028   \n",
              "1916           -698.680263 -28.979112    -28.979112    -28.979112   \n",
              "1917           -525.560255  22.073053     22.073053     22.073053   \n",
              "1918           -435.752759  -6.511852     -6.511852     -6.511852   \n",
              "1919           -289.764882  25.031293     25.031293     25.031293   \n",
              "1920           -215.996743 -11.462046    -11.462046    -11.462046   \n",
              "1921            -48.908085  49.754693     49.754693     49.754693   \n",
              "1922            -47.270918 -49.906028    -49.906028    -49.906028   \n",
              "1923             70.209958 -28.979112    -28.979112    -28.979112   \n",
              "1924            212.910006  22.073053     22.073053     22.073053   \n",
              "1925            270.906321  -6.511852     -6.511852     -6.511852   \n",
              "\n",
              "           yearly  yearly_lower  yearly_upper  multiplicative_terms  \\\n",
              "1906 -1995.054965  -1995.054965  -1995.054965                   0.0   \n",
              "1907 -1863.081349  -1863.081349  -1863.081349                   0.0   \n",
              "1908 -1728.995772  -1728.995772  -1728.995772                   0.0   \n",
              "1909 -1593.643178  -1593.643178  -1593.643178                   0.0   \n",
              "1910 -1457.817052  -1457.817052  -1457.817052                   0.0   \n",
              "1911 -1322.253096  -1322.253096  -1322.253096                   0.0   \n",
              "1912 -1187.624987  -1187.624987  -1187.624987                   0.0   \n",
              "1913 -1054.542142  -1054.542142  -1054.542142                   0.0   \n",
              "1914  -923.549408   -923.549408   -923.549408                   0.0   \n",
              "1915  -795.128522   -795.128522   -795.128522                   0.0   \n",
              "1916  -669.701150   -669.701150   -669.701150                   0.0   \n",
              "1917  -547.633308   -547.633308   -547.633308                   0.0   \n",
              "1918  -429.240907   -429.240907   -429.240907                   0.0   \n",
              "1919  -314.796175   -314.796175   -314.796175                   0.0   \n",
              "1920  -204.534697   -204.534697   -204.534697                   0.0   \n",
              "1921   -98.662778    -98.662778    -98.662778                   0.0   \n",
              "1922     2.635110      2.635110      2.635110                   0.0   \n",
              "1923    99.189070     99.189070     99.189070                   0.0   \n",
              "1924   190.836953    190.836953    190.836953                   0.0   \n",
              "1925   277.418173    277.418173    277.418173                   0.0   \n",
              "\n",
              "      multiplicative_terms_lower  multiplicative_terms_upper          yhat  \n",
              "1906                         0.0                         0.0  39946.284471  \n",
              "1907                         0.0                         0.0  40125.331313  \n",
              "1908                         0.0                         0.0  40145.612658  \n",
              "1909                         0.0                         0.0  40287.748656  \n",
              "1910                         0.0                         0.0  40460.483436  \n",
              "1911                         0.0                         0.0  40553.318974  \n",
              "1912                         0.0                         0.0  40705.346717  \n",
              "1913                         0.0                         0.0  40787.792712  \n",
              "1914                         0.0                         0.0  40965.858672  \n",
              "1915                         0.0                         0.0  40980.475326  \n",
              "1916                         0.0                         0.0  41112.686101  \n",
              "1917                         0.0                         0.0  41271.662597  \n",
              "1918                         0.0                         0.0  41347.326581  \n",
              "1919                         0.0                         0.0  41479.170947  \n",
              "1920                         0.0                         0.0  41538.795574  \n",
              "1921                         0.0                         0.0  41691.740720  \n",
              "1922                         0.0                         0.0  41679.234375  \n",
              "1923                         0.0                         0.0  41782.571739  \n",
              "1924                         0.0                         0.0  41911.128276  \n",
              "1925                         0.0                         0.0  41954.981079  "
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "forecast.tail(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cGHAMs6Zxhlm"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "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": [
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              "      <th>ds</th>\n",
              "      <th>yhat</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017-05-07</td>\n",
              "      <td>2904.324833</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-05-08</td>\n",
              "      <td>2925.581783</td>\n",
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              "      <th>2</th>\n",
              "      <td>2017-05-09</td>\n",
              "      <td>2860.851272</td>\n",
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              "      <th>3</th>\n",
              "      <td>2017-05-10</td>\n",
              "      <td>2875.782637</td>\n",
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              "      <th>4</th>\n",
              "      <td>2017-05-11</td>\n",
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              "      <th>1921</th>\n",
              "      <td>2022-08-10</td>\n",
              "      <td>41691.740720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1922</th>\n",
              "      <td>2022-08-11</td>\n",
              "      <td>41679.234375</td>\n",
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              "    <tr>\n",
              "      <th>1923</th>\n",
              "      <td>2022-08-12</td>\n",
              "      <td>41782.571739</td>\n",
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              "    <tr>\n",
              "      <th>1924</th>\n",
              "      <td>2022-08-13</td>\n",
              "      <td>41911.128276</td>\n",
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              "    <tr>\n",
              "      <th>1925</th>\n",
              "      <td>2022-08-14</td>\n",
              "      <td>41954.981079</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>1926 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             ds          yhat\n",
              "0    2017-05-07   2904.324833\n",
              "1    2017-05-08   2925.581783\n",
              "2    2017-05-09   2860.851272\n",
              "3    2017-05-10   2875.782637\n",
              "4    2017-05-11   2712.081287\n",
              "...         ...           ...\n",
              "1921 2022-08-10  41691.740720\n",
              "1922 2022-08-11  41679.234375\n",
              "1923 2022-08-12  41782.571739\n",
              "1924 2022-08-13  41911.128276\n",
              "1925 2022-08-14  41954.981079\n",
              "\n",
              "[1926 rows x 2 columns]"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "forecast[['ds','yhat']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i7uynb3NRd1l",
        "outputId": "18345220-ae3c-42ac-b578-30c2e6b7afd7"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>yhat</th>\n",
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              "      <th>0</th>\n",
              "      <td>2017-11-09</td>\n",
              "      <td>959.619356</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-11-10</td>\n",
              "      <td>946.191139</td>\n",
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              "      <th>2</th>\n",
              "      <td>2017-11-11</td>\n",
              "      <td>945.732155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017-11-12</td>\n",
              "      <td>938.384186</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-11-13</td>\n",
              "      <td>929.188656</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1735</th>\n",
              "      <td>2022-08-10</td>\n",
              "      <td>3694.015986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1736</th>\n",
              "      <td>2022-08-11</td>\n",
              "      <td>3693.445826</td>\n",
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              "    <tr>\n",
              "      <th>1737</th>\n",
              "      <td>2022-08-12</td>\n",
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              "      <th>1738</th>\n",
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              "      <th>1739</th>\n",
              "      <td>2022-08-14</td>\n",
              "      <td>3727.225852</td>\n",
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              "</table>\n",
              "<p>1740 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             ds         yhat\n",
              "0    2017-11-09   959.619356\n",
              "1    2017-11-10   946.191139\n",
              "2    2017-11-11   945.732155\n",
              "3    2017-11-12   938.384186\n",
              "4    2017-11-13   929.188656\n",
              "...         ...          ...\n",
              "1735 2022-08-10  3694.015986\n",
              "1736 2022-08-11  3693.445826\n",
              "1737 2022-08-12  3698.613653\n",
              "1738 2022-08-13  3716.530615\n",
              "1739 2022-08-14  3727.225852\n",
              "\n",
              "[1740 rows x 2 columns]"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "forecast_et[['ds','yhat']]"
      ]
    },
    {
      "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: 3096.982\n",
            "r^2 score = 0.9407211980906783\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": {
        "id": "3fX1M29Sxhln",
        "outputId": "4e2bf239-a0c2-4b74-bb19-2c7ba3f5a993"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE: 3096.982\n",
            "r^2 score = 0.9324448123008249\n"
          ]
        }
      ],
      "source": [
        "train_et = data_et['y']\n",
        "test_et = forecast_et['yhat'][:1640].values\n",
        "\n",
        "mae_et = mean_absolute_error(train_et, test_et)\n",
        "print('MAE: %.3f' % mae)\n",
        "\n",
        "\n",
        "print(f'r^2 score = {r2_score(train_et,test_et)}')"
      ]
    },
    {
      "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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z0ksu00UXuf0PP1RV1eHDC55n4sTSPonUA8jUvXj32oCFhmHElaP4zW20bAk33BB5MC0N3noLLrgAgHV/6UVVdlFr3Nclvs6sWQVtn38Oc+e6fhcAI0figvHNmsHllzvjyy9Dmzau9LF1a9RSjpU0wphoGIYRN5o3h1O9qicmTy62zevWzieygbrU+/HjItNF47DDoDcfMoWO3Ml9COGAxnqvWU3jWtvhq6/g7LPDTaWqVYOXXnIdM556Kmo85S9/KbE7TJ3quqTsa5hoGIYRN2rVVC7mLVa1O7HwKHOAytXS+YKe1B//WYk7a/TndT6kDx2Zxn3cxbOESzV16rj1B9d96zqD9OoVmblbN9dv5LHHqFt1Z9Tzl8QdVTjySDjmmBLdQrnARMMwjLjRPmc6B7KQRX+5OKb0lSvDd3SjyqZsN/BTjOj0GbyOq25qxUK+4lSuYyh/5hcA6tZ16VpO/dgpyIknFjzJP/8JGzfSZ/tbUa+xbVvM7uSNV7VoUex5ygsmGoZhlCl+M9erroIeuz4ihLCiy1kx5a1WDX7Ae6F/HXtcIzT43wB0YCZV2rZi0aPvsZpGPMo/AWX3bqhELvV//QROPz16NVm3bnDooaS/+CzBvsUNWcPhTGPt2pjdYcmS8PZ778WerzxgomEYRpny4otuPWoUdN/0Ib9wHNtrNio6k0fr1jCPg1nXsF140Kri+PFH0j7/lLu5h9l04Lrr4O+31eIhBvNnxtGN79i1C3rwJVU2ZhesmvIRccHwadP47UU3ymILlrCGxkyjI5WGPhebP0T267g4tkJWucFEwzCMMmX+fLduvGsJB26exkf0jvjyLgp/rKg/2p4BP/wQOcl3Ydx3Hxuoy5PcAoQLES9wDatozMMMYtdO5XJeI6dWXejdu/BzXXAB1KxJ5x+fpG9fmHTiP/IONRr2YMxd1YOtrfa1joEmGoZhlCn+8OJdcicArropOO1qLHlntevjBrB65hk34XfDhtC5MyxeHJlh5kz49lse4V9soTYQDljvoir38R+68htfj02jL++Tfc41kTMz5We//eCyy5C33+LdqpfQ8If3yL7gRi5lOFXXrYDp02O6j6BodO8e272XF0w0DMMoU/zxmzqGJpNbqTIz+FPeREzFIeJKCgua/NlVI91xhysZrF3rmuz27Rs5sNXTT0PVqrzEgDxT8IU9lGvJ4iAqeTGKdRfdXLwTd97pijxvvQU1a7J+4H18y8nu2LffFky/aJHrbh5g925oQDZdmUDVjH1rRCQTDcMwyhS/eqhT5enkHNye3WRwySWx569c2Rsld/hw6NMHGjVyAxy+/DJkZsIjj7iEq1Y52yWXsCU93Jw32M9CqURnJnEZr9GI1WjjJsU70LixG5/91FNhzhwq16/DSvZn4/6Hhofh9fnySxeIOeQQWLkybN+4gYW0ZgLHcP6c/8Z+8+UAEw3DMMqU/fd36+PrzyLjyA6oQseOsedPT/de/HXqwPvvux5y++8PV14JJ5/s5uLIzAz3Lr/ppgihOOwwtx4/Hrp2hc3UYTiXkU2jYodGz+OQQ1wnwGbN8oZXX9n2RPjll7wBrXJyYPn1D3gHV7LpwXCgvM2n/0dNXBvdCxY9uE+1vTXRMAyjTAmFoAZbyVi5GNq3L3H+9PToQ6sjAsOGuaDFUUc5QTnvvLBKAPfdB3/7m9s++miYMCHyFH7MpCT4orHswBNcYH7aNABuOD+bpgt+4R7uZuFhZ7Lj2VeYPDEHcnI48LthfCk9OKfTQhSB++8Pn7CshvBNEiYahmGUKaEQHITXhKpduxLnz6ueikbLlvDJJ2442s6dXdwhwJ13Fn3uvRGNy1873m38+CMAOz/4jEooozmLtzOuoAmrWfXmN/DFF9TZspz39xvAziatGF3/CjeS7s8/wxFHuBP277/30xMmCRMNwzDKFFVozUK307p1ifPnVU8VRs+ernt2Ziakp5fo3Rtz9VQAXzSW05wFtIaffgKgNx+xhBZM4UgmNjidtdSn8yf3wK23sj6jCVOancH06XDz2jvRunXdUPB+66s33nCtwsohJhqGYZQpodDeiUbt2rBpU+zpd3pDRT30UPFpi2ptWxjVq4fFZjzHoOMnwM6dnMI3fMoZgLA9pwr3cheNF0+ErCzeaXIrdRqks2wZrKAZu98c5UbSvfdep4iNG0efuKMcYKJhGEaZEgrBgSxAa9Vy/R5KSMOGsGZN8el8fNHwOwYWRXDO8FgRccIBMIaTkBXL4a67qMF2TzRc6ep5rmNx+x7w0EM8k3Eb9eu7wXQBlh1yMsybB//5j1OgCy6AL76ArVtL7lCSMdEwDKNM8UsaoQNauzduCalWrWS9qP2Og9WqFZ+2NKIB4cD8CPqxp2pNePRR1lOP7+gGuFhJLpV59/IvYNAgNm4S6tUL38fN+buHnHGG68wxdmzpHEoiJhqGYZQpoRC0ZAna8oBS5a9UKebROli2zM2nBPEVDT/Gsp0avJh+I5qWxrUMZSfuor42zp0bbiVcp46L1UO4w2Mef/kL1KjhShsl5eefI+exTTAmGoZhlCmhELRgKTRvUar8aWmxi8akSeHtWKqnipkDqlCCgfmJvR5g++K1jOL8PJv/Dn/lFddp3b/WXXe57U6d8p0wIwNOOsl1DixJJP/TT11A/bHHSn4TZYSJhmEYZUrajq3UYyNaStGoVCn2D2l/ciWIraRRitqyCBo2hN17hKmL3AQdp57q7NFae4m48EVaGrz9Nlx2WT596NnTdfqbOze2i+/ZAwMHuu0BA4pOG0diFg0RSRORKSLyqbe/n4h8IyLzvHW9QNrBIpIlInNF5LSAvbOIzPCOPS3ifkIRyRCRkZ59goi0KrtbNAwjkdTatAwAaVl60Yi1pBGs9omlpFFapkxxPcxbt4YNGyA729kbNHDraDEYPw6Sm+vmkxo+HNatCyTo2dOtY62ieucdN4Tw8OFQr17x6eNESUoaNwO/B/YHAWNUtS0wxttHRNoD/YAOQA/geRHxu9QMBQYAbb2lh2e/Etigqm2AJ4EhpbobwzASytq1riHQxo1hW80N3rR1pRSNRYvc4LWxCEfwZV1cSePQQ0vlDuCGQTn6aDcD4MaN4RKDH7OIJhqXX17Q5s9VDsABBzinYhGNUMg1123dOukTdMQkGiLSHPgb8ErA3AsY7m0PB3oH7CNUdZeqLgSygK4i0hSorarjVFWBN/Ll8c/1HnCSXwoxDCN1eeYZGDHCDTbrU2ujE41KpRSNKW7+I3+0DjZudLGB4Oi1PkFbYaLRvr2rRpo9u1TuRFC7thvQ1q8+80s3O/NNK37LLdE7ww8enM/Qq5cbBHHmTCce7drBo48WzPj5566UcdddUaLqiSXW/pH/B/wLqBWwNVbVlQCqulJE/Km5mgHjA+mWebY93nZ+u59nqXeuHBHZBNQHSjDBomEYicaPKQSrXWptXEoIQZo3i56phNx4oxst5NhjwzU6PitWhLcLq56aNatM3ABc66s9e8Ki0bixW8+ZE5muadPo+T/4IJ/hllvgySfhT38K2/71L/dgg3GLRx6BFi3goov2yv+yoFjJEpEzgDWqOqm4tH6WKDYtwl5Unvy+DBCRTBHJzPYrFQ3DSBp+T+lgVVLtTUtZTWMko5TtWz38KqDVq9062gd2cPDYWALhe4s/xIkvGm3aRE9XVM/zO+4IlJAaNYKhQ912s2auOVinTnDTTS4QAi6Y8tNPcOutpW/+VYbEUs45DjhLRBYBI4DuIvIWsNqrcsJb+304lwHBcmlzYIVnbx7FHpFHRCoDdYBg7R8AqvqSqnZR1S4NGzaM6QYNw0gs1dYtZVnEv3rpmDzZiZFfUR2tZWowlhDPQLiPP5iiH+SuWRNatSqY7u23Cz/Hgw/mq4G6/HJ3c8uWOcEYNcrZr7nGXeicc1ww5aqryuo29opiRUNVB6tqc1VthQtwj1XVi4HRQH8vWX/AH31rNNDPaxHVGhfwnuhVZW0RkWO8eMWl+fL45+rrXaN8DgFpGBWQZ5+Fhd5wUyxdylJKF88IcvXVrrnq11+7/WjDpW/bFt5OVEkjJydc0khLi37dPn3C27/8An/8EXk82HCgAAcdBP/9r4t1pKe7OriBA51CpQB7E1F5GDhFROYBp3j7qOosYBQwG/gSuF5V/VbX1+KC6VnAfMBvNjAMqC8iWcCteC2xDMNIbYLNVcaNA1RpzUIWUvKBCosjWgul4DSyiayeesVrEpSWFn24dX/MKYA//xnatoX64ckFix+i/bbbXMcOcCURv5dgClCigYJV9Xvge297HXBSIekeAB6IYs8EDoti3wmcWxJfDMNIPsFOeKrAqlVUYydbGhxY5teKJhrBkkZpRrAtKb5o/Pqr209Lc/028hPNl2DdSbENoNLS4LXX3KRTSW4tlZ/U8sYwjHJFgZ7QCxYAkN6u7EXDD4gHCYpGIhrpp6dHXjMtzU1fnp9oY1zVrRvejjozYTRSTDDARMMwjL0g+PWvSp5obI5DSWPlyoK2bdtcf7fPPy/zy0Ulf+OlwqqZoomGH5uBcjkieh4mGoZhlJpg5zpV2DJ9ASGEz2aWboTboog2vtP27a5Tdf7+G/GiKNHYbz83hfjPP0fGL3wOOsiNiA5w5JHx8zHemGgYhlFqCojG1AUspxmz5pd9+9doPcK3bXMjjCeK/KIR3P/1V9fA6bjjCs//0ktuncSRzfcaEw3DMEpN8EX+ww9QM3sBCzgwooNzSXnyyeKv5bNtW3hWvUQQnGO8Vi0nEj//DC++GH3YkMLyxxzTSEFMNAzDKDXBF/mrr0LGcicaTzxR+nNec03x1/LZvj2xJQ2/ie9FF7kxqMCVLGIdqdwXuJLMgZ5qmGgYhlFqgoHwhqwhY+0KZtN+r5q/FjZSxu7dbsy+YGwj0dVTfgC7tCWpGjXc4LaLF5edT4nGRMMwjFIT/Po/lnEA/Mqf90o0CmuRNGKEG+vpllvcfm6uG102GaKxN52zZ8wIxzbKIyYahmGUmqBo/JlfyU1LZxKd49rR7rnnXEzArypKZEyjLESjVq3i06QyJhqGYZSaoGh0Zywr9+/MLqqWuWgEx3ICV+rwRSORJQ2/V3ft2om7ZqpRomFEDMMwgviicSFvcxSZfN72MVha9kN65P86z8kJ98xOpGg8/jg0aRLub1ERsZKGYRilZtcuOJlveJuLWUgr+o29Gth70fjpJ/dy9sl/vueeC0+dmkjRaNwYHnssJaa1SBomGoZhlJrcnXt4GScU3fiOLbh6m70Vjb/8xQ0b4p8n//kyM+Gf/3TbiYxpGCYahmHsBe3X/UQrFnMuo1hMqzx7WVVP+eP1Rfuynz7drRNZ0jBMNAzD2AuOXvsZeypVof87kYM/ldUseuec49bRmuH6QmKikVhMNAzDKDVHr/ucmfX/Sk7VyDaolcuoic3rr8OcOdFHjfWHSjfRSCwmGoZhlIoNS7bQNncO72cfz9q1Yfv995fdNdLS3JhOwRn6Lr44Mo3FNBKLiYZhGKVi8y8zAJjGEREli+BkQ2VFUDTefBMaNAjvW0kjsZhoGIZRKkJTpgEwncMjhvoOhcr+WkHRALj0UrfOyHDzWBiJw0TDMIxSUXXOVDZQl6vvbVlwrvAyJn/nPn84j127UnJG1H0a6xFuGEapqLrod2ZyGCf+VZg5M2yPR0ljyBBYujQcz4hl7gojPhSr0SJSVUQmisg0EZklIv/17PuJyDciMs9b1wvkGSwiWSIyV0ROC9g7i8gM79jTIm4qeBHJEJGRnn2CiLQq+1s1DKMsqbY8i3m0JT2duFdP1awJo0fDeee5/RtuKPtrGLERS8FuF9BdVY8AOgI9ROQYYBAwRlXbAmO8fUSkPdAP6AD0AJ4XEb+V9VBgANDWW3p49iuBDaraBngSGFIG92YYRrzYto2q61eSRRvS06Fbt/CheIhGfqI1wTUSQ7GioQ6vBpF0b1GgFzDcsw8HenvbvYARqrpLVRcCWUBXEWkK1FbVcaqqwBv58vjneg84yS+FGIaRgsyfD5AnGh06wEcfuUOdOiXRLyPuxBRCEpE0EZkKrAG+UdUJQGNVXQngrRt5yZsBSwPZl3m2Zt52fntEHlXNATYB9QTY3sAAACAASURBVEtzQ4ZhJICsLLfyRAOgVy83XlT37kn0y4g7MQXCVTUX6CgidYEPReSwIpJHKyFoEfai8kSeWGQArnqLli1bFumzYRhxxBON+RwUMS5UcGTaeHPEEWU3XIkROyVqrKaqG4HvcbGI1V6VE956jZdsGdAikK05sMKzN49ij8gjIpWBOsD6KNd/SVW7qGqXhg0blsR1wzDKkqwsdtRqyGbqxHWWvqKYOhXGj0/OtSsysbSeauiVMBCRasDJwBxgNNDfS9Yf+NjbHg3081pEtcYFvCd6VVhbROQYL15xab48/rn6AmO9uIdhGKlIVhbZtduQlgZNmybbGSORxFI91RQY7rWAqgSMUtVPRWQcMEpErgSWAOcCqOosERkFzAZygOu96i2Aa4HXgWrAF94CMAx4U0SycCWMfmVxc4ZhxImsLJZX+yuNGpXd4IRG+aDYn1tVpwNHRrGvA04qJM8DwANR7JlAgXiIqu7EEx3DMFKcHTtg6VIWHtI2Ygwoo2JgHfANwygZCxcC8EeojYlGBcREwzCMkjF3LgAzd5poVERMNAzDKBkzZoAIE7Z2wBoxVjxMNAzDKBnTp6MHHcTyDdWtpFEBMdEwDKNkzJjB9oMORzU+Ey4ZqY2JhmEYsbN9O2Rl8euWPwGwZEmS/TESjomGYRixM3s2hEJUPtKJxoABSfbHSDgmGoZhxM4MNy/46saHA1CnTjKdMZKBiYZhGLEzYwZUq0Z2rQMBkjbulJE8TDQMw4id336Dww9nx243r5qNMlvxMNEwDCM2Nm2CcePg5JPZscOZTDQqHiYahmHExpgxbjLw005j82aoUQPS0orPZuxb2PiUhmHExpdfQu3ajFpyDE88kWxnjGRhJQ3DMIpn92745BM46STuvj+9+PTGPouJhmEYxTN2LKxaBRdeiE2PVrEx0TAMo3g++QSqV4czzmD1amfq1Cm5LhnJwUTDMIyiCYWcaJxyClStSrt2zvzpp8l1y0gOJhqGYRTN99/D0qXQty/gwhtnnmlzg1dUTDQMwyiar792E4H37g3Azp3WP6MiY6JhGEbRjBkDxxwDNWsCJhoVnWJFQ0RaiMh3IvK7iMwSkZs9+34i8o2IzPPW9QJ5BotIlojMFZHTAvbOIjLDO/a0iIhnzxCRkZ59goi0KvtbNQyjxKxfD5MmwUkn5ZlMNCo2sZQ0coB/qOqhwDHA9SLSHhgEjFHVtsAYbx/vWD+gA9ADeF5E/H6jQ4EBQFtv6eHZrwQ2qGob4ElgSBncm2EYe8tHH4EqnHFGnslEo2JTrGio6kpVnextbwF+B5oBvYDhXrLhQG9vuxcwQlV3qepCIAvoKiJNgdqqOk5VFXgjXx7/XO8BJ/mlEMMwksjPP0P9+tC5c55pxw4TjYpMiWIaXrXRkcAEoLGqrgQnLEAjL1kzYGkg2zLP1szbzm+PyKOqOcAmoH5JfDMMIw788gscdxx433CqVtKo6MQsGiJSE3gfGKiqm4tKGsWmRdiLypPfhwEikikimdnZ2cW5XC7YuTPZHhhGIaxZA3/8Accdx9q1MGqUa24LJhoVmZhEQ0TScYLxtqp+4JlXe1VOeOs1nn0Z0CKQvTmwwrM3j2KPyCMilYE6wPr8fqjqS6raRVW7NGzYMBbXU5pZs6BaNXj//WR7YhhRePVVtz7hBPr1g/PPD4uFiUbFJZbWUwIMA35X1eDYlqOB/t52f+DjgL2f1yKqNS7gPdGrwtoiIsd457w0Xx7/XH2BsV7cY58mK8ut778/uX4YRgE+/BAGD4YTT+SL9UczZkzk4S5dkuOWkXxiGRr9OOASYIaITPVs/wYeBkaJyJXAEuBcAFWdJSKjgNm4llfXq2qul+9a4HWgGvCFt4ATpTdFJAtXwui3l/dVLqhWza3XFyhTGUYSycmBW25x26NGcXrjgrXHJhoVl2JFQ1V/JnrMAeCkaEZVfQB4IIo9Ezgsin0nnuhUJPzZz/b9MpVRrnj3XVi8GD74gG01GkVNUr16gn0yUgbrEZ4kli51rRnBRMNIMYYMgUMPhV69rBRsFMBm7ksSLVuGt33R+OMPqFcP9oEYv1FeWbQIpk2Dp56CSpX49dfIw2PHwtatSfHMSBFMNFIAVVeN3K4ddOvm/jENIyl8/71bd+8OwNq1kYe7dUusO0bqYdVTKYBq+Ovtu++S64tRwfn+e2jQANq3B8ibcAngnHOS45KRWlhJIwVQhV27ku2FYeBE44QToJL7nlyzxlWXfvYZHHBAcl0zUgMraaQA27fDpk3J9sKo8Cxa5FpN/fWvgBut4MUXYcMGOOooaBS9IZVRwTDRSAE2byZvCk3DSBo//ujWJ54IQGam283JSZI/RkpiomEYhmPyZNcBo0MHVq6E44935jvvTK5bRmphopFkatRItgeG4TF1Khx+OKSl8fTTYfPddyfPJSP1MNFIMpdeWtC2alXi/TAqOKpONDp2BGDduvChytZcxghgopEkjjoKevRwHW/z89xziffHqODMn+9aY3TqBEDdus78+ONJ9MlISUw0ksSePZCe7po05sdGvTUSzsSJbn300YD720xLg1tvTaJPRkpiopEkfNFIT0+2J4YBTJjgguBep76xY21QQiM6JhpJIifH1RWfeWb049u2JdYfo4IzcaKbB7xyZZo3h/HjYcuWZDtlpCImGknCL2kceWR4iPQgNg2skTB274YpU+DoowmFYPlyZ37yyeS6ZaQmJhpJwi9pAGRkuHX16jBwoNsONnk0SseKFSACI0Yk25MUZ/p0N45N165cfbUzZWSE/xYNI4iJRpLwSxrgXmwvvACTJsGf/uRs996bPN/2FaZNc+vHHoMBA2DmzOT6k7L4QfCuXZk82W0GByo0jCAmGkkiKBoAf/87HHIIVKmSPJ/2NXbvdutJk+Dll6FPn+T6kyxyc6Ffv/CkXwWYMAEaN4aWLdm5E849F+rUSaiLRjnCRCNJ7NkTvdNUUDRGj06cP/si+UcOnjcvvH3nna6Et2BBCU44eza0auUmyF65sixcjCtr18Ijj7i/s5Ej3bAgnTvDccflSzhxInTtCiJs3GiCYRSNiUYSWL/e9aNq1qzgsWDpo1cvyMpy28uWuQDlf/7jqluM4ok23Lw/S+ID3gz2gwbFeLLNm+HUU90osJMmuaapU6eWiZ/x4qij4PbbI22TJxM5G9+mTTBnDjsO70q7dm40goMPTqibRjnDRCPe/PQTNG/uPmvPPhs2b8571xx5ZCDdV1/BNddQJzdyUuZVqyAUghYt3Gnuvx/++c/EuV+eidYqLX8z0nffjfFkL7zgVPuDD+Dbb2HjRrjkkoJT26UQixaFt4/vsoNRnIsiDOMKNKRMmgQrRvwAwCdrjuGPP1za005LvK9GOUJVi1yAV4E1wMyAbT/gG2Cet64XODYYyALmAqcF7J2BGd6xpwHx7BnASM8+AWhVnE+qSufOnTXlWbFCtXp1VfeB65YGDfS5q6eoiGp2tpfu99/zju85u29E8k8/VX399chTQFLvqtxwzz0Fn9uFF6rm5KhWqxa29e0bw8n+9CfVY48N73/yiaqI6vnnx83/veH99wN/Q6NzNdS5c8SDWHDevxRUv+ZkXU1DrcxuTU9XHTMm2Z4b8QbI1BjesYUtsZQ0Xgd65LMNAsaoaltgjLePiLQH+gEdvDzPi0ial2coMABo6y3+Oa8ENqhqG+BJYEgMPpUPHnnEzbA0bpz7Vx02DNaupc+I82jbfAcNGnjpHnrIrXv2pPJH77NmwkJ/3Di2bnVVUwewiHbMyTu1X83Cpk0wa1bAYPhEm2v9f/9z4ynt2BGeie699+CZZ1xhUAR++y1fprlzYcYMFyH2OeMMuOkmFywYPz5mn/bsSUzhZOFCtx47Fv629AVk0iQ4/XQee2gPIzif1qMe4QX+zil8ywj6kUM63brlTQ1uGIUTi7IArYgsacwFmnrbTYG5Gi5lDA6k+wo41kszJ2C/AHgxmMbbrgysxSuFFLWkfEkjFFJt0kS1T59I+8cfq4K+UfdGt791q2qNGqpXXaU6b577Ehw6VBctcps9e6q+84/fVEG3Ul1bsUBBdeNGVV2zJvz1eN99Cb/FVOegg1R79FB96CHVm24KP6r99nPrU04pWBIJLrt3eycaPFg1LU112bLIC2zZolq/vmq3bpH2UEj17bdV//ijgNk/90MPFeL077+rZmUVMIdC4e3t2939bNhQ+L0/8oi7zua1u1QbNlQ98EDVnBzt10+1Fpt0HfVUQXdSRZuxVFu1Ul21qvDzGfsO7GVJo7SisTHf8Q3e+lng4oB9GNAX6AJ8G7AfD3zqbc8EmgeOzQcaFOdTyovG9Onu8b72WoR5wwbVt7hQc6ikmpmpev/9Lt0PP7g3Q9Omqv366Y4dztyokWrmQeflvW2e5ToF1fnzVfWOO8JvoSpVVJcu1WXLVBcsUG3cWPW335Jy5ylDjRqqt9zitjdsiBSEWrVUX365aNF4/33vREccoXriidEv4v9+kyeHbY895mxpae7H8JgwIfL8d96Z71w//xw++O23eeYNL47U9+ijN561SEeMCAsCqD73nNOuHTsiT3XHHa72LPedkS7hxx+rquratW63PTN1+5+O0ufuWK5btpT82Rrll1QTjeeiiMY5wFFRROMTb3tWFNGoX4gfA4BMILNly5bxeqZlwxNPuMe7ZEmEeepU1fpk6470muH//O7dw5+SF13klCIU0iOPVD3/1PWaI2k6tOY/9E0u0o3U1ups1cMP2urS9ezpXkxpaaoDBkS8lOrVS8J9pwjr17tn8PDDbj/4le+/z0Mh1c2bVde+8ZmO5gztyviIvI89pq50AapDhuh//+s2//1v1Z07vQtt2ODiVhdc4PZXrYoMmNxxh6qq3nVX2FSZ3XoEUxRCkU6femo4UaNG7tzffptnm8IRmsaeqAJ38smqM2eq3nxzWCD3bxpygnfAAS6QYxiaPNGw6qniOP101YMPLmD2aqd03pBApHLmzHCCYcPybPXrq57Du6qgV7X/RZeP+FEV9DJe1f685tJ98onLd/nlqmlpeiST9C7uUQW9if/TdesSc7u6e7fqrl0JuljxNGniHs8HH4Rtl1+u+sILqi1bqo4c6RmHDIl8+37+uebkuM177lHVV15xO9OnRySLqJG66SanQllZqoMGuQTjx6uefLKG2rTRnTvD+apVU93ao48q6JMM1Mcf937+hQtd0eDuu12Jo1KlvEzrmhyq1/GsKmhvPiiydBRcPrh9vNt45plEPXajHJAs0XgUGORtDwIe8bY7ANNwLaJaAwuANO/Yb8AxgABfAKd79uuBF7ztfsCoWHxKadHYvdvVjVx3XYFDzzzjnvrq1epeshs3RibIynIJhg5VUH2Rq3UjtTWNPaqhkIYOPVRnc4hO4QidSXs964xcl2/ZMg1VTi/w5njluknxv9/XXgtf87PP4n+9Qnj2WdUbbww/QtCiRfP77yP9rl3bqc3mzVq9uuptt6lqnz4aat5cNRQq8FL+4gvvPIsXu9/bP+C1qNr5yFOqoC1ZlHfou9cWRpykLXP1hBPUFUVEVBctcuf85z/z0rRnpqaxR9dRT0dwXl4paP161VmzwjoVLLhecIFq6PobVDMyCv6NGRWauIsG8A6wEtgDLMO1dqqPazU1z1vvF0h/h1fFNBfoGbB38eIX873Yh9/ktirwLq7J7UTgwFgcT2nR+Okn92jzKsXD3HabatWqkYHNCEIhF6m9/HI979yQLuQA/YDe7pdSVX3++by3w4W8Fbar6rPHvqWraKTjq56os37I1i3U0Je50r38imHVKnfqQv0qjJUrI99Y6emqM2aU8CR7z/btYRcaNnQf/l7NUHRCIdWuXV2GFSuczf/dbrlFmzZV7XLIFt1WuZa+yNU6dqxqzZouRnLuueFr5QWPg4EG78Wf+foMVdD+vKbgChD673+riuj8N37WPaTpEwzUdm1yVJs3Vz3ttEgfFy/W83rtzDvtc1yrezKqawY7CsTKd+3K99vt3u0eREztiY2KREJKGqm4pLRo3H23+2pcvz7CPHly+L1SJOedp9qggX7xxCxV0Gt5Lvw+ycnRP64aor34MOJr+ptvIs+9bZvqCM7TFTRRIbfYmqMLLnB5a9YsYfX3vfe6jDNmuOh87dqqrVs7BxLElVdG6lagpqlwPvvMexM/F2k//3zV9HTtkD5XB+LiUsfwa945//tfl8yPUbz8ciDv0qWqe/aoqit8QEhX01Df4GIF11BOjzgir25rxYn9dJPU1tubvelO9u67Bdy8886wOM145HONLOIUwede2g8/LD6tUaEw0UhFjjtO9aijCpj9F0+tWsXkHz1aFXTt4X9VBW3N/Ih4em5u5Mvxuutc/Dy/IG1+7g1V0M78pj/8oDpwYLj2Iz/nnx/OP3t2jPeZm6vaqpXqSSeFbe+9504yaFCMJ4mRUMg1/cln2/3ex1qPdVFFo6gmqdqtm2rdugXFbdkyV1ryTjKWv0ac8957w+7sv7/q0Ue7qqLc3MjTPP20S/8O52uo6f66c0fI9eYUCTePnjQp78Q51WtGjQmdcorTYVV1TaSqV49a7VmAiy5yLSHyIvaG4TDRSDU2b1atXDnipfnZZ6oNGhR8qRfKrl3uHx50Il2i5pkyJeKdo61aufX8+YFE2dkaEtG7uCcvXdOmBc8V7O4BrhWp7tihetllqi1b6rwrHoyI1efx3Xcuw//+F2k/7zz3DApTqJISrEp66qmw7fTTVUHn01qrsU0hshd4ocyc6RI88kj044EqwOwPf9Rdu1QvvdSZhgwJJzvssMjnNmSI6iWXuI7jedVX973kNubMUX3DiXiwLXTW31211i8DR+bZJk1yXTx+/93Fw3v2DPjWt6/rG+KLwdChrrl1sC5u2bK81nSGkR8TjVTj00/dY/Xa2QdbzkSrDSmUoUN1w2F/0UOZpR06FJ4seO4zzih4PHTssXnC4y/5P2j94PwFF7g+YOnsUm3RIuLkV1R6reDJ//EP98LK39B//nz30rrhhhhvthiCgXYRV/Xy1FOR/vGKfv21S75nT8Ev/wj+8x/3Ni6qN9usWRF9L9asUb3++sgsLVtGPv/8S716Go7KP/ecC4Y0bRrh3Ny57vDbb7v9zZsLnidCsL/80hlHjnQllmDCa65xaQYNcs8pqtIbFR0TjWSxapWr4M7XD0NvvNG1WNm+XVUj+2uBq72JlTlzXJ4HHig8TfDcUcXFe7Hc1n+N3nJLOO0LL7jOZm3auA9XPzh/+umqj3CbKmj22VdrVbbrZDrqFmpo7upsfegh1X/9S3XsmJBuaXawbjvu5OjB84svdgGSvAG2PHJzSx5t//OfXV+DtWtdwNi/icMP1+P/nKPTOUyXN+kU27lCIdcUunv3kvkQhSlTnBvDhrl3uO9W375u/eST3vVatlQ980xXz3TVVRHnmD/fpX31Vddyt06dgqIRQU6Oexb+wdq13XP529/cfkaGW/frt9f3Z+ybmGgkA/9t4S/+KG+7d7smm7165SV98cXIpOPHl+xSWVlFfzX7/T4gslNyHuPGqf9lmr9HcnDp0sUln/vlAlXQMXTLO9a/0zTNoZI+wm3h9zVTVUEH8IJWq+bei23auHr+779X19s9f9FqyxbVTp1cZ4WFC6PfUP6bXbnSfTX7EejZszWvPi4rS//9b9Xr8YpKU6cW/0CnOr/1xReLT1tC/Bo0VdcGIk8br746/KD9fjUeS5Y488CB0X+XH36IcqHf3LAyeuSRqps2OdvWrS5O4wvH8uVlfn/GvoGJRqLJzVU99FD36I47Lvzf/csvrn4ZVD/+WEOhiKrxvCVvPKMyZPv2IoK+e/a4z9dLL1XVwkXD/wDefeOtqqCHMkvBjd80caLq/+inO6mi9cnWww5T/fiwwbqHNG3AGj3xxMhzdeyo7o3ZsaPq4YeHfXnwwXCic84p6GtWlgv+dOsWFg+/GmratKi317u3ascWa13wOpa2xX//u6s6y18CKgN27nTVSwVYtcrdQ7t2BUQxf4tlUH38cRdSKtXwHjNnWu9vo0hMNBLNww+7x/bWW25/yRL3Jez/x7dooauXFxzqoXt3LVjVkCiuvtp93a9fr5qTo9OeHKONWKXgXnLvvuvVpi1erFq5si4/4Xy9+OLIcfMmvDxNFXTbdf9wb8dGjXTrn0/RuXPd8U2bXKzE/6heu1bDqvn9907V6tZ1gZeBAyOq8PI455zwA3v1VWfr2NGVTvJVaa1Z46rY8qryTz01sgd+To4rnTz6aPhFvXWr8+G888ry6cZOlGo5fyyo4DJlShJ8MyoMJhqJJMfrhHXooZFfjH51Qc2aqmPH5jWS8Zf333dfjvlbjCaMYAcRbwllZOjsD+dEpvM7BcyZE/08/fo5gdx/f5cuSh8AfxSUCy9U15y1Zk03MJJfdTJ5cjiYG+xIsWuXS/v3v7vmR40ahasBn3hCVd0H+wMPhEf28JeZM9VVg4FrepSb66pu/ARnn+1+gBtucPs//VQ2z7UM2Lgx7Gbz5oUWqAyjzDDRSCQffeQe2ahRqur+4b2+XE5QvJ0HHnDJRo92rVJTggEDwm+ndu3cukOH8A1s2+aa+5x+euHnyMoKTypVyKiv/sCAeXHmhx4KX7d3b2fbscOVfAYODGf0m+9+9JHqr7+G81SqpLp4ccTLNbj4rY50wwbXAeaMM8Kj/3bp4joaBjMcemgpur3Hj23bwq499liyvTEqAiYaieTkk12TyR078ppGtmvn9fT1CIXcJG9NmiTevWKZMydcUT58uLsBv+PB44+7/S+/LPocfquxIircjz/etcZSVSdKffuqXnFFZFvfU05xHR18br/d9e3wgwL9+jl/nn9eVSMDxUcd5TpFFxjm6tZbw4natg231LriCmerVy/lJo3wB0cEi10bicFEI1GsW6ealqaTewzWE05wVe3+P/sll7gky5eHR1e9557EuldiQiEnghCutgkO0b4X+M/lv/+NFNQI7rvPVXX5rX/yz1mRmxsR87joIhe/LpJt21Svvdb9OPlFbefOQLEwtQiFUqrwY+zjmGgkCm+i7k5kRq0m8Ztb+kvS4hclYdGivJ7nCm7iqDLAj2v4i2q4e8Xtt7u49hcDA3GNFStUQffc/5Aee6ybQCoz0zUn7tMnHJ7o2rVM3DOMCo2JRqI49VTd06KVQigv0Ltzp+r//V/kC7JOnXIiGD5z5riqoMWLy+yU+cfGCg4A6y9V2KkbqKszO1+qG55ygtz/iClRBRlci1q/x7dhGKVnb0XDH5683NGlSxfNzMxMzMXWrIGmTRlSaTCDcu4nMxM6d3aHQiFISwsnXb4c9t8/MW6lMsOHw9y58NBDBY8deCAsWAAvcTX9GMFYutOViezPClq1Enr3hk2boGdPOPFEqF8fKlUCkcTfh2Hsa4jIJFXtUtr8lcvSmXLBqlXwxRdw/vlQvToAW7bAm2/CaafBQQdFJlcFff9DKoVCvB06D4Ajjggfr1QpvJ2bG7lfkenf362rVYO77oL0dCcE1ao5+x9/QKNZF1Krzyv0YjTftr2GuZ8KbduaOBhGKlPxShr33gt33w1XXAHDhgGQkQG7d4eT9O8PzZrBccfB3/4G33ISzVnGIcxh6VKhefPIU44f79K3aLEXN7QPs2mTe74NG+Y7kJsLXbvC5Mnw++9wyCFJ8c8wKhJ7W9KoeKKRnQ3HHgvz5/OvPlk8+sFBRSZvyBpW0pQH+TcbbrmPJ54opcNGdHJy3FK1arI9MYwKwd6KRsWrTGnYkI0f/0BIKlH3g2F55uxseOIJmDULbrwR/vEPZz+bD0kjxM0/nWuCEQ8qVzbBMIxyRIUraWRlQdu28BG9OJoJnNVxKe+8l+5iGaoweza0aQMZGbw7SjmsXwfaHphL5XlzrLLdMIxyj5U0Ssh777n12DYDaMJqJv7nEycYu3fDYYe5pWpV+PBDzl39LIfq71S+9WYTDMMwDFKopCEiPYCngDTgFVV9uKj0e9PkdvlyaNYkF1q1gg4d4MsvXZ3Us88WTNyokctQueI1NDMMY99jnyhpiEga8BzQE2gPXCAi7eN1vWbNcJ0rrroKvvoKbrvNCcbRR7uOF+vXw8EHwwEHwDffmGAYhmF4pERJQ0SOBe5R1dO8/cEAqhqla5ijTDr3LVsGLVu6WAbA6tWuZGEYhrGPsk+UNIBmwNLA/jLPFl+aN4fXXoMLLzTBMAzDiIFUqXeJFmUuUAQSkQHAAICWLVuWzZX79w93XzYMwzCKJFVKGsuAYH/q5sCK/IlU9SVV7aKqXRoW6F5sGIZhxJtUEY3fgLYi0lpEqgD9gNFJ9skwDMPIR0pUT6lqjojcAHyFa3L7qqrOSrJbhmEYRj5SQjQAVPVz4PNk+2EYhmEUTqpUTxmGYRjlABMNwzAMI2ZMNAzDMIyYMdEwDMMwYiYlhhEpDSKSDSwuZfYGwNoydKesSWX/Utk3SG3/Utk3SG3/Utk3KF/+HaCqpe7oVm5FY28Qkcy9GXsl3qSyf6nsG6S2f6nsG6S2f6nsG1Qs/6x6yjAMw4gZEw3DMAwjZiqqaLyUbAeKIZX9S2XfILX9S2XfILX9S2XfoAL5VyFjGoZhGEbpqKglDcMwDKMUVDjREJEeIjJXRLJEZFASrt9CRL4Tkd9FZJaI3OzZ7xGR5SIy1VtOD+QZ7Pk7V0ROS4CPi0RkhudHpmfbT0S+EZF53rpeov0TkXaB5zNVRDaLyMBkPjsReVVE1ojIzICtxM9KRDp7zzxLRJ4WkWhzzJSFb4+KyBwRmS4iH4pIXc/eSkR2BJ7hC/H0rQj/SvxbJvDZjQz4tUhEpnr2ZDy7wt4j8f/bU9UKs+BG0J0PHAhUAaYB7RPsQ1Ogk7ddC/gDNy/6PcBtUdK39/zMAFp7/qfF2cdFQIN8tkeAdazn7AAAA5FJREFUQd72IGBIsvwL/JargAOS+eyAE4BOwMy9eVbAROBY3IRkXwA94+TbqUBlb3tIwLdWwXT5zlPmvhXhX4l/y0Q9u3zHHwfuSuKzK+w9Eve/vYpW0ugKZKnqAlXdDYwAeiXSAVVdqaqTve0twO8UPbVtL2CEqu5S1YVAFu4+Ek0vYLi3PRzonWT/TgLmq2pRHTzj7puq/gisj3LdmJ+ViDQFaqvqOHX/xW8E8pSpb6r6tarmeLvjcROeFUq8fCvMvyJI+rPz8b7EzwPeKeoccX52hb1H4v63V9FEIzlzkReCiLQCjgQmeKYbvGqDVwPFymT4rMDXIjJJ3BS7AI1VdSW4P1jAn1A9Wc+0H5H/tKny7KDkz6qZt51oP6/AfVn6tBaRKSLyg4gc79mS4VtJfstk+Hc8sFpV5wVsSXt2+d4jcf/bq2iiEdNc5IlARGoC7wMDVXUzMBQ4COgIrMQVfyE5Ph+nqp2AnsD1InJCEWkT7p+42R3PAt71TKn07IqiMH+S8QzvAHKAtz3TSqClqh4J3Ar8T0RqJ8G3kv6WyfiNLyDygyVpzy7Ke6TQpIX4UmIfK5poxDQXebwRkXTcD/22qn4AoKqrVTVXVUPAy4SrURLus6qu8NZrgA89X1Z7RVm/2L0mWf7hxGyyqq72/EyZZ+dR0me1jMhqorj6KSL9gTOAi7wqCbxqi3Xe9iRcnffBifatFL9lop9dZaAPMDLgc1KeXbT3CAn426toopH0uci9+tBhwO+q+kTA3jSQ7GzAb7UxGugnIhki0hpoiwtcxcu/GiJSy9/GBU5nen7095L1Bz5Ohn8eEV96qfLsApToWXnVCFtE5Bjv7+PSQJ4yRUR6ALcDZ6nq9oC9oYikedsHer4tSKRv3rVL9Fsm2j/gZGCOquZV6STj2RX2HiERf3tlEckvTwtwOq6lwXzgjiRc/y+44t90YKq3nA68Cczw7KOBpoE8d3j+zqWMWl8U4d+BuFYW04BZ/jMC6gNjgHneer8k+VcdWAfUCdiS9uxw4rUS2IP7aruyNM8K6IJ7Qc4HnsXreBsH37Jwddv+394LXtpzvN97GjAZODOevhXhX4l/y0Q9O8/+OnBNvrTJeHaFvUfi/rdnPcINwzCMmKlo1VOGYRjGXmCiYRiGYcSMiYZhGIYRMyYahmEYRsyYaBiGYRgxY6JhGIZhxIyJhmEYhhEzJhqGYRhGzPw/fX1nLOPdiO4AAAAASUVORK5CYII=\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": {
        "id": "qcjvthzxxhlp",
        "outputId": "e5c2a128-ad75-48cf-d8ac-92b043a7abc2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot =m_et.plot(forecast_et)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UHFInXaAxhlp"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i9bBm8I4xhlq"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ngZ5h1hOxhlq"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LvurLYJVxhlq"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zwh2dQEjxhlq"
      },
      "outputs": [],
      "source": [
        "#pip install dash"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "Oe0fehyjtbyw",
        "outputId": "ef59f7c3-92b7-415c-8e18-ceb2e3bf560e"
      },
      "outputs": [
        {
          "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": "bIMa3ahNxhls",
        "outputId": "bc610739-1ca9-4995-aef8-5356ee95170a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot2 = m_et.plot_components(forecast_et)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P10TjK3_xhls"
      },
      "source": [
        "# SIMPLE SMOOTHING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qLw2-v4Bxhlt"
      },
      "outputs": [],
      "source": [
        "#!pip install pandas==1.1.3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SDkf2Bfqxhlt"
      },
      "outputs": [],
      "source": [
        "import pandas_datareader as pdr\n",
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "import pandas as pd\n",
        "from statsmodels.tsa.statespace.exponential_smoothing import ExponentialSmoothing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Tu34Ub4xhlt"
      },
      "outputs": [],
      "source": [
        "df = pdr.get_data_yahoo('BTC-USD')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k2oQftLCxhlt"
      },
      "outputs": [],
      "source": [
        "df.drop(['High', 'Low', 'Open', 'Volume', 'Adj Close'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ah7xXOq1xhlu"
      },
      "outputs": [],
      "source": [
        "df=df.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U3IPPBQBxhlu",
        "outputId": "f2951a01-c9bb-4792-921c-76296ab1c15b"
      },
      "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>Date</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1821</th>\n",
              "      <td>2022-05-02</td>\n",
              "      <td>38529.328125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1822</th>\n",
              "      <td>2022-05-03</td>\n",
              "      <td>37750.453125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1823</th>\n",
              "      <td>2022-05-04</td>\n",
              "      <td>39698.371094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1824</th>\n",
              "      <td>2022-05-05</td>\n",
              "      <td>36575.140625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1825</th>\n",
              "      <td>2022-05-06</td>\n",
              "      <td>36293.035156</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date         Close\n",
              "1821 2022-05-02  38529.328125\n",
              "1822 2022-05-03  37750.453125\n",
              "1823 2022-05-04  39698.371094\n",
              "1824 2022-05-05  36575.140625\n",
              "1825 2022-05-06  36293.035156"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[\"Date\"] = pd.to_datetime(df[\"Date\"])\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PFFP92NVxhlv",
        "outputId": "11a493f3-89bb-4a64-852d-9763f9263611"
      },
      "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>Close</th>\n",
              "      <th>mean</th>\n",
              "      <th>mean_se</th>\n",
              "      <th>mean_ci_lower</th>\n",
              "      <th>mean_ci_upper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1826</th>\n",
              "      <td>36303.964179</td>\n",
              "      <td>983.218147</td>\n",
              "      <td>35043.919424</td>\n",
              "      <td>37564.008934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1827</th>\n",
              "      <td>36303.964179</td>\n",
              "      <td>1370.215962</td>\n",
              "      <td>34547.961768</td>\n",
              "      <td>38059.966590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1828</th>\n",
              "      <td>36303.964179</td>\n",
              "      <td>1669.810062</td>\n",
              "      <td>34164.016481</td>\n",
              "      <td>38443.911877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1829</th>\n",
              "      <td>36303.964179</td>\n",
              "      <td>1923.288720</td>\n",
              "      <td>33839.170509</td>\n",
              "      <td>38768.757849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1830</th>\n",
              "      <td>36303.964179</td>\n",
              "      <td>2147.047591</td>\n",
              "      <td>33552.411977</td>\n",
              "      <td>39055.516381</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Close          mean      mean_se  mean_ci_lower  mean_ci_upper\n",
              "1826   36303.964179   983.218147   35043.919424   37564.008934\n",
              "1827   36303.964179  1370.215962   34547.961768   38059.966590\n",
              "1828   36303.964179  1669.810062   34164.016481   38443.911877\n",
              "1829   36303.964179  1923.288720   33839.170509   38768.757849\n",
              "1830   36303.964179  2147.047591   33552.411977   39055.516381"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "HORIZON = 10\n",
        "\n",
        "model = ExponentialSmoothing(endog = df['Close'])\n",
        "results = model.fit()\n",
        "\n",
        "preds = results.get_forecast(steps=HORIZON)\n",
        "\n",
        "preds.summary_frame(alpha=0.2).head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yp6btTUnxhlv",
        "outputId": "03638f38-e0ca-4a24-c2ac-23c306e5fd79"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1826    36303.964179\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(results.forecast(steps=1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T3Rz10zlxhlv",
        "outputId": "9a1d7eb2-f788-4399-9c60-64645df60828"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1826    36303.964179\n",
            "dtype: float64\n",
            "No\n"
          ]
        }
      ],
      "source": [
        "x = results.forecast(steps=1)\n",
        "print(x)\n",
        "if x.item() > 36996.509921:\n",
        "    print('yes')\n",
        "else:\n",
        "    print('No')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DU6r84K_xhlw",
        "outputId": "c9146083-a4e8-4b2d-d498-cea39db0f88a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x22d5bdf65e0>"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = df['Close'].iloc[-25:].plot(figsize=(12,4))\n",
        "forecast_80 = preds.summary_frame(alpha=0.2)[['mean', 'mean_ci_lower', 'mean_ci_upper']]\n",
        "forecast_90 = preds.summary_frame(alpha=0.1)[['mean', 'mean_ci_lower', 'mean_ci_upper']]\n",
        "\n",
        "ax.fill_between(forecast_90.index, forecast_90['mean_ci_lower'], forecast_90['mean_ci_upper'], alpha=0.5, label='90% PI')\n",
        "ax.fill_between(forecast_80.index, forecast_90['mean_ci_lower'], forecast_80['mean_ci_upper'], alpha=0.5, label='80% PI')\n",
        "\n",
        "forecast_80['mean'].iloc[-25:].plot(ax=ax, label='forecast', color='red')\n",
        "\n",
        "results.fittedvalues.iloc[-25:]\n",
        "\n",
        "results.fittedvalues.iloc[-25:].plot(ax=ax, label='fitted', color='green', linestyle='--')\n",
        "\n",
        "ax.legend(loc=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RZmUV39nxhlw",
        "outputId": "048ca183-67f2-4e3c-d6f9-63cdc5fd90f6"
      },
      "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>Date</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017-05-07</td>\n",
              "      <td>1596.709961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-05-08</td>\n",
              "      <td>1723.349976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2017-05-09</td>\n",
              "      <td>1755.359985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017-05-10</td>\n",
              "      <td>1787.130005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-05-11</td>\n",
              "      <td>1848.569946</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date        Close\n",
              "0 2017-05-07  1596.709961\n",
              "1 2017-05-08  1723.349976\n",
              "2 2017-05-09  1755.359985\n",
              "3 2017-05-10  1787.130005\n",
              "4 2017-05-11  1848.569946"
            ]
          },
          "execution_count": 116,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rJYHLqPcxhlw",
        "outputId": "f6f2e533-2bd2-4e1e-a549-23bdd48468f5"
      },
      "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>Close</th>\n",
              "      <th>mean</th>\n",
              "      <th>mean_ci_lower</th>\n",
              "      <th>mean_ci_upper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1826</th>\n",
              "      <td>36303.964179</td>\n",
              "      <td>35043.919424</td>\n",
              "      <td>37564.008934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1827</th>\n",
              "      <td>36303.964179</td>\n",
              "      <td>34547.961768</td>\n",
              "      <td>38059.966590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1828</th>\n",
              "      <td>36303.964179</td>\n",
              "      <td>34164.016481</td>\n",
              "      <td>38443.911877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1829</th>\n",
              "      <td>36303.964179</td>\n",
              "      <td>33839.170509</td>\n",
              "      <td>38768.757849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1830</th>\n",
              "      <td>36303.964179</td>\n",
              "      <td>33552.411977</td>\n",
              "      <td>39055.516381</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Close          mean  mean_ci_lower  mean_ci_upper\n",
              "1826   36303.964179   35043.919424   37564.008934\n",
              "1827   36303.964179   34547.961768   38059.966590\n",
              "1828   36303.964179   34164.016481   38443.911877\n",
              "1829   36303.964179   33839.170509   38768.757849\n",
              "1830   36303.964179   33552.411977   39055.516381"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "forecast_80.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nnWFNdJ6xhlx",
        "outputId": "eda89b50-0ab7-42ad-80a6-2571000e2026"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Exponential Smoothing Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>         <td>Close</td>      <th>  No. Observations:  </th>    <td>1826</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>             <td>ETS(A, N, N)</td>   <th>  Log Likelihood     </th> <td>-15173.639</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>            <td>Fri, 06 May 2022</td> <th>  AIC                </th>  <td>30353.278</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                <td>16:25:36</td>     <th>  BIC                </th>  <td>30369.808</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th>  <td>30359.376</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                      <td> - 1826</td>     <th>  Scale              </th> <td>966717.924</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>      <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "         <td></td>            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>smoothing_level</th> <td>    0.9706</td> <td>    0.015</td> <td>   65.955</td> <td> 0.000</td> <td>    0.942</td> <td>    0.999</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>initial_level</th>   <td> 1596.7100</td> <td> 2.59e+05</td> <td>    0.006</td> <td> 0.995</td> <td>-5.06e+05</td> <td> 5.09e+05</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (Q):</th>          <td>136.20</td> <th>  Jarque-Bera (JB):  </th> <td>8631.90</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                 <td>0.00</td>  <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th>  <td>13.93</td> <th>  Skew:              </th>  <td>-0.15</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>     <td>0.00</td>  <th>  Kurtosis:          </th>  <td>13.65</td> \n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                        Exponential Smoothing Results                         \n",
              "==============================================================================\n",
              "Dep. Variable:                  Close   No. Observations:                 1826\n",
              "Model:                   ETS(A, N, N)   Log Likelihood              -15173.639\n",
              "Date:                Fri, 06 May 2022   AIC                          30353.278\n",
              "Time:                        16:25:36   BIC                          30369.808\n",
              "Sample:                             0   HQIC                         30359.376\n",
              "                               - 1826   Scale                       966717.924\n",
              "Covariance Type:                  opg                                         \n",
              "===================================================================================\n",
              "                      coef    std err          z      P>|z|      [0.025      0.975]\n",
              "-----------------------------------------------------------------------------------\n",
              "smoothing_level     0.9706      0.015     65.955      0.000       0.942       0.999\n",
              "initial_level    1596.7100   2.59e+05      0.006      0.995   -5.06e+05    5.09e+05\n",
              "===================================================================================\n",
              "Ljung-Box (Q):                      136.20   Jarque-Bera (JB):              8631.90\n",
              "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
              "Heteroskedasticity (H):              13.93   Skew:                            -0.15\n",
              "Prob(H) (two-sided):                  0.00   Kurtosis:                        13.65\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
              "\"\"\""
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "results.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N71QOwYoxhlx"
      },
      "outputs": [],
      "source": [
        "from statsmodels.graphics.tsaplots import plot_pacf\n",
        "from statsmodels.graphics.tsaplots import plot_acf\n",
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm_notebook\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from itertools import product\n",
        "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
        "from sklearn.metrics import median_absolute_error, mean_squared_log_error\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eJsO-Y1lxhlx",
        "outputId": "728257d1-c045-4f99-b93d-2dae9f1d7271"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x864 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Diagnosing the model residuals\n",
        "results.plot_diagnostics(figsize = (15, 12))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UrGkCOjVxhlx",
        "outputId": "9117b5ed-cf0b-41e3-9f01-9ff50addc14e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((1552, 2), (274, 2))"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Divide into train and validation set to calculate R-squared score\n",
        "train = df[:int(0.85 * (len(df)))]\n",
        "test  = df[int(0.85 * (len(df))):]\n",
        "\n",
        "train.shape, test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R6CZcJV6xhly",
        "outputId": "bcbec6ca-69bb-4f12-c085-c5aed1a70c34"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1552    40835.256373\n",
            "1553    42758.318325\n",
            "1554    44503.015481\n",
            "1555    43818.817400\n",
            "1556    46290.618721\n",
            "            ...     \n",
            "1821    38447.746023\n",
            "1822    38526.932365\n",
            "1823    37773.255400\n",
            "1824    39641.837685\n",
            "1825    36665.197985\n",
            "Name: Simple Exponential Smoothing Predictions, Length: 274, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "start = len(train)\n",
        "end   = len(train) + len(test) - 1\n",
        "predictions = results.predict(start = start, end = end, dynamic = False, typ = 'levels').rename('Simple Exponential Smoothing Predictions')\n",
        "print(predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "clpsyCZhxhly",
        "outputId": "89b57cd7-43d7-4e0d-f533-7287650a7eca"
      },
      "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>r2_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.958025</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   r2_score\n",
              "0  0.958025"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create a dataframe to store r2 score\n",
        "evaluation_results = pd.DataFrame({'r2_score': r2_score(test['Close'], predictions)}, index = [0])\n",
        "\n",
        "# Display the results\n",
        "evaluation_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fV8SE-Dqxhlz"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MS0HLiPCxhlz"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kNU0G3o-xhlz"
      },
      "outputs": [],
      "source": [
        "#pip install dash_bootstrap_components"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g_UrIogJxhlz"
      },
      "outputs": [],
      "source": [
        "#pip uninstall dash"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rDWopWYbevPJ"
      },
      "outputs": [],
      "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",
        "from dash import Dash, dcc, html, Input, Output, State,callback_context, dash_table\n",
        "\n",
        "\n",
        "\n",
        "app = dash.Dash()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UX3CTL_8xhl0"
      },
      "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",
        "        html.H1(id = 'H3',children = 'Bitcoin', style = {'textAlign':'center',\\\n",
        "                                            'marginTop':40,'marginBottom':40}),\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",
        "html.Div([       \n",
        "            \n",
        "        html.H1(id = 'H2',children = 'Ethereum ', style = {'textAlign':'center',\\\n",
        "                                            'marginTop':40,'marginBottom':40}),\n",
        "        dcc.Dropdown( id = 'dropdown_et',\n",
        "        options = [\n",
        "            {'label':'Prophet', 'value':'Profs' },\n",
        "            {'label': 'ARIMA', 'value':'arms'},\n",
        "            {'label': 'Smoothing', 'value':'smts'},\n",
        "            ],\n",
        "        value = 'arms', style = {'background-color': 'lightblue'}),\n",
        "        dcc.Graph(id = 'bar_plot_et')\n",
        "        \n",
        "    ]),\n",
        "    \n",
        "\n",
        "html.Div([\n",
        "    html.Button('10 Days Prediction Prophet model', id='btn-nclicks-1', n_clicks=0, style={'font-size': '12px', 'width': '140px', 'display': 'inline-block', 'margin-bottom': '10px', 'margin-right': '5px', 'height':'37px', 'verticalAlign': 'top'}),\n",
        "    html.Button('10 Days Prediction Arima model', id='btn-nclicks-2', n_clicks=0, style={'font-size': '12px', 'width': '140px', 'display': 'inline-block', 'margin-bottom': '10px', 'margin-right': '5px', 'height':'37px', 'verticalAlign': 'top'}),\n",
        "    html.Button('10 Days Prediction Smoothing model', id='btn-nclicks-3', n_clicks=0, style={'font-size': '12px', 'width': '140px', 'display': 'inline-block', 'margin-bottom': '10px', 'margin-right': '5px', 'height':'37px', 'verticalAlign': 'top'}),\n",
        "    html.Div(id='container-button-timestamp'),\n",
        "    \n",
        "], )\n",
        "    #style={'display':'flex','justify-content':'center', 'align-items':'center'}\n",
        "\n",
        "\n",
        "\n",
        "    \n",
        "   \n",
        "    ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w9GWn9B8xhl0"
      },
      "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",
        "        \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 = forecast_et['ds'],y = forecast_et['yhat'],\\\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 = forecast['ds'],y = forecast['yhat'],\\\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",
        "                                 \n",
        "    elif dropdown_value == 'smt':\n",
        "        fig_three = go.Figure()\n",
        "        \n",
        "        fig_three.add_trace(go.Scatter(x = df['date'],y =  df['Close'].iloc[-25:],\\\n",
        "                     line = dict(color = 'green', width = 4)\n",
        "                     ))\n",
        "        fig_three.add_trace(go.Scatter(x = forecast_80['Close'], y = forecast_80['mean'].iloc[:25],\\\n",
        "                     line = dict(color = 'firebrick', width = 4)\n",
        "                     ))\n",
        "        #fig_three.add_trace(go.Scatter(y = results.fittedvalues.iloc[-25:],\\\n",
        "                     #line = dict(color = 'blue', width = 4)\n",
        "                     #))\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": "_lQpyAqExhl1"
      },
      "outputs": [],
      "source": [
        "@app.callback(Output(component_id='bar_plot_et', component_property= 'figure'),\n",
        "              [Input(component_id='dropdown_et', component_property= 'value')])\n",
        "\n",
        "def graph_update(dropdown_value):\n",
        "    print(dropdown_value)\n",
        "    \n",
        "    if dropdown_value == 'Profs':\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 = forecast_et['ds'],y = forecast_et['yhat'],\\\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 == 'arms':\n",
        "\n",
        "        fig_two = go.Figure([go.Scatter(x = forecast['ds'],y = forecast['yhat'],\\\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",
        "                                 \n",
        "    elif dropdown_value == 'smts':\n",
        "        fig_three = go.Figure()\n",
        "        \n",
        "        fig_three.add_trace(go.Scatter(x = df['date'],y =  df['Close'].iloc[-25:],\\\n",
        "                     line = dict(color = 'green', width = 4)\n",
        "                     ))\n",
        "        fig_three.add_trace(go.Scatter(x = forecast_80['Close'], y = forecast_80['mean'].iloc[:25],\\\n",
        "                     line = dict(color = 'firebrick', width = 4)\n",
        "                     ))\n",
        "        #fig_three.add_trace(go.Scatter(y = results.fittedvalues.iloc[-25:],\\\n",
        "                     #line = dict(color = 'blue', width = 4)\n",
        "                     #))\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')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tnhAr69nxhl1"
      },
      "outputs": [],
      "source": [
        "@app.callback(\n",
        "    Output('container-button-timestamp', 'children'),\n",
        "    Input('btn-nclicks-1', 'n_clicks'),\n",
        "    Input('btn-nclicks-2', 'n_clicks'),\n",
        "    Input('btn-nclicks-3', 'n_clicks')\n",
        ")\n",
        "def displayClick(btn1, btn2, btn3):\n",
        "    changed_id = [p['prop_id'] for p in callback_context.triggered][0]\n",
        "    if 'btn-nclicks-1' in changed_id:\n",
        "        \n",
        "        \n",
        "        msg = 'Predictions for 10 days'\n",
        "\n",
        "        fig = go.Figure(data=[go.Table(\n",
        "            header=dict(values=['Date','Price'],\n",
        "                        #fill_color='paleturquoise',\n",
        "                        align='left'), \n",
        "            cells=dict(values=[forecast['ds'][1826:1836], forecast['yhat'][1826:1836]],\n",
        "               #fill_color='lavender',\n",
        "               align='left'))\n",
        "        ])\n",
        "        return dcc.Graph(figure=fig)\n",
        "        \n",
        "        \n",
        "                      \n",
        "\n",
        "    elif 'btn-nclicks-2' in changed_id:\n",
        "        msg = 'Button 2 was most recently clicked'\n",
        "    elif 'btn-nclicks-3' in changed_id:\n",
        "        msg = 'Button 3 was most recently clicked'\n",
        "    else:\n",
        "        msg = 'Button 3 was most recently clicked'\n",
        "        \n",
        "    return html.Div(msg)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LUb5gz6Cxhl2",
        "outputId": "4b2df9da-d2ce-4f7e-9bcf-97061f73bf3e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            "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",
            "INFO:werkzeug:127.0.0.1 - - [06/May/2022 21:18:18] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [06/May/2022 21:18:18] \"\u001b[37mGET /_dash-layout HTTP/1.1\u001b[0m\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [06/May/2022 21:18:18] \"\u001b[37mGET /_dash-dependencies HTTP/1.1\u001b[0m\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [06/May/2022 21:18:18] \"\u001b[37mGET /_dash-component-suites/dash/dcc/async-dropdown.js HTTP/1.1\u001b[0m\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [06/May/2022 21:18:18] \"\u001b[37mGET /_dash-component-suites/dash/dcc/async-graph.js HTTP/1.1\u001b[0m\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [06/May/2022 21:18:18] \"\u001b[37mGET /_dash-component-suites/dash/dcc/async-plotlyjs.js HTTP/1.1\u001b[0m\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [06/May/2022 21:18:18] \"\u001b[37mPOST /_dash-update-component HTTP/1.1\u001b[0m\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [06/May/2022 21:18:19] \"\u001b[37mPOST /_dash-update-component HTTP/1.1\u001b[0m\" 200 -\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "armarms\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:werkzeug:127.0.0.1 - - [06/May/2022 21:18:19] \"\u001b[37mPOST /_dash-update-component HTTP/1.1\u001b[0m\" 200 -\n"
          ]
        }
      ],
      "source": [
        "\n",
        "if __name__ == '__main__': \n",
        "    app.run_server()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ztSIqUoqxhl2"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eHBo89t6xhl2"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z-4ysfjgxhl2"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Prophet_Model (2) (1).ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}