btc.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "btc.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNz5UI3qxzoycAmwq82mM7l",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/somiljain7/AI/blob/master/btc.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install seaborn\n",
        "!pip install fbprophet\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2nPcyiVNEexj",
        "outputId": "2abaee34-e11e-44a4-fd82-09f64338d80f"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (0.11.2)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.3.5)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.21.6)\n",
            "Requirement already satisfied: matplotlib>=2.2 in /usr/local/lib/python3.7/dist-packages (from seaborn) (3.2.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (1.4.3)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (3.0.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (4.1.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->seaborn) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=2.2->seaborn) (1.15.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: fbprophet in /usr/local/lib/python3.7/dist-packages (0.7.1)\n",
            "Requirement already satisfied: Cython>=0.22 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (0.29.30)\n",
            "Requirement already satisfied: cmdstanpy==0.9.5 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (0.9.5)\n",
            "Requirement already satisfied: pystan>=2.14 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (2.19.1.1)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (1.21.6)\n",
            "Requirement already satisfied: pandas>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (1.3.5)\n",
            "Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (3.2.2)\n",
            "Requirement already satisfied: LunarCalendar>=0.0.9 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (0.0.9)\n",
            "Requirement already satisfied: convertdate>=2.1.2 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (2.4.0)\n",
            "Requirement already satisfied: holidays>=0.10.2 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (0.10.5.2)\n",
            "Requirement already satisfied: setuptools-git>=1.2 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (2.8.2)\n",
            "Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (4.64.0)\n",
            "Requirement already satisfied: pymeeus<=1,>=0.3.13 in /usr/local/lib/python3.7/dist-packages (from convertdate>=2.1.2->fbprophet) (0.5.11)\n",
            "Requirement already satisfied: korean-lunar-calendar in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->fbprophet) (0.2.1)\n",
            "Requirement already satisfied: hijri-converter in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->fbprophet) (2.2.4)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->fbprophet) (1.15.0)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from LunarCalendar>=0.0.9->fbprophet) (2022.1)\n",
            "Requirement already satisfied: ephem>=3.7.5.3 in /usr/local/lib/python3.7/dist-packages (from LunarCalendar>=0.0.9->fbprophet) (4.1.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->fbprophet) (1.4.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->fbprophet) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->fbprophet) (3.0.9)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=2.0.0->fbprophet) (4.1.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install search_sampler"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x8DoSlelE15K",
        "outputId": "6a5da812-d0c2-42f9-ddb5-4a5d48dd4a21"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting search_sampler\n",
            "  Downloading search_sampler-1.0.1-py3-none-any.whl (11 kB)\n",
            "Collecting google-api-python-client==1.6.5\n",
            "  Downloading google_api_python_client-1.6.5-py2.py3-none-any.whl (53 kB)\n",
            "\u001b[K     |████████████████████████████████| 53 kB 1.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.2.0 in /usr/local/lib/python3.7/dist-packages (from search_sampler) (1.3.5)\n",
            "Requirement already satisfied: httplib2<1dev,>=0.9.2 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.6.5->search_sampler) (0.17.4)\n",
            "Requirement already satisfied: oauth2client<5.0.0dev,>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.6.5->search_sampler) (4.1.3)\n",
            "Requirement already satisfied: six<2dev,>=1.6.1 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.6.5->search_sampler) (1.15.0)\n",
            "Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.6.5->search_sampler) (3.0.1)\n",
            "Requirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from oauth2client<5.0.0dev,>=1.5.0->google-api-python-client==1.6.5->search_sampler) (4.8)\n",
            "Requirement already satisfied: pyasn1-modules>=0.0.5 in /usr/local/lib/python3.7/dist-packages (from oauth2client<5.0.0dev,>=1.5.0->google-api-python-client==1.6.5->search_sampler) (0.2.8)\n",
            "Requirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.7/dist-packages (from oauth2client<5.0.0dev,>=1.5.0->google-api-python-client==1.6.5->search_sampler) (0.4.8)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.2.0->search_sampler) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.2.0->search_sampler) (2022.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.2.0->search_sampler) (1.21.6)\n",
            "Installing collected packages: google-api-python-client, search-sampler\n",
            "  Attempting uninstall: google-api-python-client\n",
            "    Found existing installation: google-api-python-client 1.12.11\n",
            "    Uninstalling google-api-python-client-1.12.11:\n",
            "      Successfully uninstalled google-api-python-client-1.12.11\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "firebase-admin 4.4.0 requires google-api-python-client>=1.7.8, but you have google-api-python-client 1.6.5 which is incompatible.\n",
            "earthengine-api 0.1.315 requires google-api-python-client<2,>=1.12.1, but you have google-api-python-client 1.6.5 which is incompatible.\u001b[0m\n",
            "Successfully installed google-api-python-client-1.6.5 search-sampler-1.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "xq7eUTfxEcw8"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import sys\n",
        "import fbprophet\n",
        "import datetime\n",
        "import holidays\n",
        "import numpy as np \n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.dates as mdates\n",
        "import matplotlib.pyplot as pyplot\n",
        "from datetime import date\n",
        "from statistics import mode\n",
        "from fbprophet import Prophet\n",
        "from fbprophet.diagnostics import cross_validation\n",
        "from fbprophet.diagnostics import performance_metrics\n",
        "from matplotlib import pyplot as plt\n",
        "from statsmodels.tsa import seasonal\n",
        "from statsmodels.tsa import stattools\n",
        "from search_sampler import SearchSampler\n",
        "from pandas.plotting import autocorrelation_plot\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LassoLarsCV\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import r2_score\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "btc=pd.read_csv('btc.csv')"
      ],
      "metadata": {
        "id": "GsR8v6QbE9UU"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "btc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "jvB2wIPmFwUT",
        "outputId": "9a4dd3f9-2f23-48b6-d2dc-acc06e6708dc"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      date  BTC\n",
              "0      2019-01-01 00:00:00   47\n",
              "1      2019-01-01 01:00:00   33\n",
              "2      2019-01-01 02:00:00   44\n",
              "3      2019-01-01 03:00:00   40\n",
              "4      2019-01-01 04:00:00   42\n",
              "...                    ...  ...\n",
              "26433  2021-12-31 20:00:00   94\n",
              "26434  2021-12-31 21:00:00   76\n",
              "26435  2021-12-31 22:00:00   65\n",
              "26436  2021-12-31 23:00:00   51\n",
              "26437  2022-01-01 00:00:00   65\n",
              "\n",
              "[26438 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a633e014-2cc7-46e0-ab39-cb2f8111fe82\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>BTC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01 00:00:00</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-01 01:00:00</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-01 02:00:00</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-01 03:00:00</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-01 04:00:00</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26433</th>\n",
              "      <td>2021-12-31 20:00:00</td>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26434</th>\n",
              "      <td>2021-12-31 21:00:00</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26435</th>\n",
              "      <td>2021-12-31 22:00:00</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26436</th>\n",
              "      <td>2021-12-31 23:00:00</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26437</th>\n",
              "      <td>2022-01-01 00:00:00</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>26438 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a633e014-2cc7-46e0-ab39-cb2f8111fe82')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a633e014-2cc7-46e0-ab39-cb2f8111fe82 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a633e014-2cc7-46e0-ab39-cb2f8111fe82');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "btc['Dates'] = pd.to_datetime(btc['date']).dt.date"
      ],
      "metadata": {
        "id": "JAKnIe6pFw8q"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "btc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "E_hbSRfhGZCS",
        "outputId": "a316607c-f08b-4f02-867d-5149ba8c20c6"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      date  BTC       Dates\n",
              "0      2019-01-01 00:00:00   47  2019-01-01\n",
              "1      2019-01-01 01:00:00   33  2019-01-01\n",
              "2      2019-01-01 02:00:00   44  2019-01-01\n",
              "3      2019-01-01 03:00:00   40  2019-01-01\n",
              "4      2019-01-01 04:00:00   42  2019-01-01\n",
              "...                    ...  ...         ...\n",
              "26433  2021-12-31 20:00:00   94  2021-12-31\n",
              "26434  2021-12-31 21:00:00   76  2021-12-31\n",
              "26435  2021-12-31 22:00:00   65  2021-12-31\n",
              "26436  2021-12-31 23:00:00   51  2021-12-31\n",
              "26437  2022-01-01 00:00:00   65  2022-01-01\n",
              "\n",
              "[26438 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6125c838-7843-4462-9b74-56855495d7d7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>BTC</th>\n",
              "      <th>Dates</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01 00:00:00</td>\n",
              "      <td>47</td>\n",
              "      <td>2019-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-01 01:00:00</td>\n",
              "      <td>33</td>\n",
              "      <td>2019-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-01 02:00:00</td>\n",
              "      <td>44</td>\n",
              "      <td>2019-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-01 03:00:00</td>\n",
              "      <td>40</td>\n",
              "      <td>2019-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-01 04:00:00</td>\n",
              "      <td>42</td>\n",
              "      <td>2019-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26433</th>\n",
              "      <td>2021-12-31 20:00:00</td>\n",
              "      <td>94</td>\n",
              "      <td>2021-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26434</th>\n",
              "      <td>2021-12-31 21:00:00</td>\n",
              "      <td>76</td>\n",
              "      <td>2021-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26435</th>\n",
              "      <td>2021-12-31 22:00:00</td>\n",
              "      <td>65</td>\n",
              "      <td>2021-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26436</th>\n",
              "      <td>2021-12-31 23:00:00</td>\n",
              "      <td>51</td>\n",
              "      <td>2021-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26437</th>\n",
              "      <td>2022-01-01 00:00:00</td>\n",
              "      <td>65</td>\n",
              "      <td>2022-01-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>26438 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6125c838-7843-4462-9b74-56855495d7d7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6125c838-7843-4462-9b74-56855495d7d7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6125c838-7843-4462-9b74-56855495d7d7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prophetdf=pd.DataFrame()\n",
        "prophetdf['ds']=btc['date']\n",
        "prophetdf['y']=btc['BTC']"
      ],
      "metadata": {
        "id": "KeE-uMNyGZKA"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prophetdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "6iR4wK52H_Rs",
        "outputId": "8b2abc70-05ff-470f-fc10-9a9bc8ca754b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        ds   y\n",
              "0      2019-01-01 00:00:00  47\n",
              "1      2019-01-01 01:00:00  33\n",
              "2      2019-01-01 02:00:00  44\n",
              "3      2019-01-01 03:00:00  40\n",
              "4      2019-01-01 04:00:00  42\n",
              "...                    ...  ..\n",
              "26433  2021-12-31 20:00:00  94\n",
              "26434  2021-12-31 21:00:00  76\n",
              "26435  2021-12-31 22:00:00  65\n",
              "26436  2021-12-31 23:00:00  51\n",
              "26437  2022-01-01 00:00:00  65\n",
              "\n",
              "[26438 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2e56c894-6768-4683-b594-b685f2f94a71\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01 00:00:00</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-01 01:00:00</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-01 02:00:00</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-01 03:00:00</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-01 04:00:00</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26433</th>\n",
              "      <td>2021-12-31 20:00:00</td>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26434</th>\n",
              "      <td>2021-12-31 21:00:00</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26435</th>\n",
              "      <td>2021-12-31 22:00:00</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26436</th>\n",
              "      <td>2021-12-31 23:00:00</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26437</th>\n",
              "      <td>2022-01-01 00:00:00</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>26438 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2e56c894-6768-4683-b594-b685f2f94a71')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2e56c894-6768-4683-b594-b685f2f94a71 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2e56c894-6768-4683-b594-b685f2f94a71');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m=Prophet()\n",
        "m.fit(prophetdf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QFHahw7XIDYC",
        "outputId": "afc99ecd-7a57-41f5-89b4-3fad02f3b052"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<fbprophet.forecaster.Prophet at 0x7fbe66bdb850>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "future=m.make_future_dataframe(periods=365)\n",
        "future.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1A08iduKIX4K",
        "outputId": "464aa7a5-7e52-4f51-8b35-0c68aaabfa5c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              ds\n",
              "26642 2022-12-28\n",
              "26643 2022-12-29\n",
              "26644 2022-12-30\n",
              "26645 2022-12-31\n",
              "26646 2023-01-01"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-907d3fdc-65a6-40a9-892d-cc4bbb1069e4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>26642</th>\n",
              "      <td>2022-12-28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26643</th>\n",
              "      <td>2022-12-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26644</th>\n",
              "      <td>2022-12-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26645</th>\n",
              "      <td>2022-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26646</th>\n",
              "      <td>2023-01-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-907d3fdc-65a6-40a9-892d-cc4bbb1069e4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-907d3fdc-65a6-40a9-892d-cc4bbb1069e4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-907d3fdc-65a6-40a9-892d-cc4bbb1069e4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast=m.predict(future)\n",
        "forecast"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "rqYgtlgNImOH",
        "outputId": "10c0b271-f357-4b51-dc0b-9c981a52da4e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       ds      trend  yhat_lower  yhat_upper  trend_lower  \\\n",
              "0     2019-01-01 00:00:00  55.405466   35.426932   73.749382    55.405466   \n",
              "1     2019-01-01 01:00:00  55.404734   33.657459   74.219193    55.404734   \n",
              "2     2019-01-01 02:00:00  55.404001   35.005107   73.976547    55.404001   \n",
              "3     2019-01-01 03:00:00  55.403269   39.823593   76.445750    55.403269   \n",
              "4     2019-01-01 04:00:00  55.402536   42.506345   81.002449    55.402536   \n",
              "...                   ...        ...         ...         ...          ...   \n",
              "26642 2022-12-28 00:00:00  62.698148   18.183788   98.222626    29.719234   \n",
              "26643 2022-12-29 00:00:00  62.709968   19.354455   96.749316    29.543842   \n",
              "26644 2022-12-30 00:00:00  62.721788   19.084095   99.130364    29.410255   \n",
              "26645 2022-12-31 00:00:00  62.733608   19.013339   94.958848    29.233522   \n",
              "26646 2023-01-01 00:00:00  62.745428   15.278281   93.490470    28.994897   \n",
              "\n",
              "       trend_upper  additive_terms  additive_terms_lower  \\\n",
              "0        55.405466       -1.181216             -1.181216   \n",
              "1        55.404734       -1.981772             -1.981772   \n",
              "2        55.404001       -0.729490             -0.729490   \n",
              "3        55.403269        2.339389              2.339389   \n",
              "4        55.402536        5.739037              5.739037   \n",
              "...            ...             ...                   ...   \n",
              "26642    93.759326       -3.631916             -3.631916   \n",
              "26643    93.890325       -3.172654             -3.172654   \n",
              "26644    94.021324       -3.430405             -3.430405   \n",
              "26645    94.152324       -3.841214             -3.841214   \n",
              "26646    94.283323       -7.119926             -7.119926   \n",
              "\n",
              "       additive_terms_upper     daily  ...    weekly  weekly_lower  \\\n",
              "0                 -1.181216 -3.085075  ...  2.154192      2.154192   \n",
              "1                 -1.981772 -3.946791  ...  2.195770      2.195770   \n",
              "2                 -0.729490 -2.743576  ...  2.225254      2.225254   \n",
              "3                  2.339389  0.287700  ...  2.243275      2.243275   \n",
              "4                  5.739037  3.660496  ...  2.250546      2.250546   \n",
              "...                     ...       ...  ...       ...           ...   \n",
              "26642             -3.631916 -3.085075  ...  1.488286      1.488286   \n",
              "26643             -3.172654 -3.085075  ...  1.531517      1.531517   \n",
              "26644             -3.430405 -3.085075  ...  0.832271      0.832271   \n",
              "26645             -3.841214 -3.085075  ... -0.037581     -0.037581   \n",
              "26646             -7.119926 -3.085075  ... -3.784518     -3.784518   \n",
              "\n",
              "       weekly_upper    yearly  yearly_lower  yearly_upper  \\\n",
              "0          2.154192 -0.250333     -0.250333     -0.250333   \n",
              "1          2.195770 -0.230751     -0.230751     -0.230751   \n",
              "2          2.225254 -0.211168     -0.211168     -0.211168   \n",
              "3          2.243275 -0.191586     -0.191586     -0.191586   \n",
              "4          2.250546 -0.172005     -0.172005     -0.172005   \n",
              "...             ...       ...           ...           ...   \n",
              "26642      1.488286 -2.035127     -2.035127     -2.035127   \n",
              "26643      1.531517 -1.619096     -1.619096     -1.619096   \n",
              "26644      0.832271 -1.177601     -1.177601     -1.177601   \n",
              "26645     -0.037581 -0.718557     -0.718557     -0.718557   \n",
              "26646     -3.784518 -0.250333     -0.250333     -0.250333   \n",
              "\n",
              "       multiplicative_terms  multiplicative_terms_lower  \\\n",
              "0                       0.0                         0.0   \n",
              "1                       0.0                         0.0   \n",
              "2                       0.0                         0.0   \n",
              "3                       0.0                         0.0   \n",
              "4                       0.0                         0.0   \n",
              "...                     ...                         ...   \n",
              "26642                   0.0                         0.0   \n",
              "26643                   0.0                         0.0   \n",
              "26644                   0.0                         0.0   \n",
              "26645                   0.0                         0.0   \n",
              "26646                   0.0                         0.0   \n",
              "\n",
              "       multiplicative_terms_upper       yhat  \n",
              "0                             0.0  54.224250  \n",
              "1                             0.0  53.422962  \n",
              "2                             0.0  54.674511  \n",
              "3                             0.0  57.742658  \n",
              "4                             0.0  61.141573  \n",
              "...                           ...        ...  \n",
              "26642                         0.0  59.066232  \n",
              "26643                         0.0  59.537314  \n",
              "26644                         0.0  59.291384  \n",
              "26645                         0.0  58.892395  \n",
              "26646                         0.0  55.625503  \n",
              "\n",
              "[26647 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2849a288-78f5-41fc-93b9-999d3e3895eb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>daily</th>\n",
              "      <th>...</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>0</th>\n",
              "      <td>2019-01-01 00:00:00</td>\n",
              "      <td>55.405466</td>\n",
              "      <td>35.426932</td>\n",
              "      <td>73.749382</td>\n",
              "      <td>55.405466</td>\n",
              "      <td>55.405466</td>\n",
              "      <td>-1.181216</td>\n",
              "      <td>-1.181216</td>\n",
              "      <td>-1.181216</td>\n",
              "      <td>-3.085075</td>\n",
              "      <td>...</td>\n",
              "      <td>2.154192</td>\n",
              "      <td>2.154192</td>\n",
              "      <td>2.154192</td>\n",
              "      <td>-0.250333</td>\n",
              "      <td>-0.250333</td>\n",
              "      <td>-0.250333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>54.224250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-01 01:00:00</td>\n",
              "      <td>55.404734</td>\n",
              "      <td>33.657459</td>\n",
              "      <td>74.219193</td>\n",
              "      <td>55.404734</td>\n",
              "      <td>55.404734</td>\n",
              "      <td>-1.981772</td>\n",
              "      <td>-1.981772</td>\n",
              "      <td>-1.981772</td>\n",
              "      <td>-3.946791</td>\n",
              "      <td>...</td>\n",
              "      <td>2.195770</td>\n",
              "      <td>2.195770</td>\n",
              "      <td>2.195770</td>\n",
              "      <td>-0.230751</td>\n",
              "      <td>-0.230751</td>\n",
              "      <td>-0.230751</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>53.422962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-01 02:00:00</td>\n",
              "      <td>55.404001</td>\n",
              "      <td>35.005107</td>\n",
              "      <td>73.976547</td>\n",
              "      <td>55.404001</td>\n",
              "      <td>55.404001</td>\n",
              "      <td>-0.729490</td>\n",
              "      <td>-0.729490</td>\n",
              "      <td>-0.729490</td>\n",
              "      <td>-2.743576</td>\n",
              "      <td>...</td>\n",
              "      <td>2.225254</td>\n",
              "      <td>2.225254</td>\n",
              "      <td>2.225254</td>\n",
              "      <td>-0.211168</td>\n",
              "      <td>-0.211168</td>\n",
              "      <td>-0.211168</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>54.674511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-01 03:00:00</td>\n",
              "      <td>55.403269</td>\n",
              "      <td>39.823593</td>\n",
              "      <td>76.445750</td>\n",
              "      <td>55.403269</td>\n",
              "      <td>55.403269</td>\n",
              "      <td>2.339389</td>\n",
              "      <td>2.339389</td>\n",
              "      <td>2.339389</td>\n",
              "      <td>0.287700</td>\n",
              "      <td>...</td>\n",
              "      <td>2.243275</td>\n",
              "      <td>2.243275</td>\n",
              "      <td>2.243275</td>\n",
              "      <td>-0.191586</td>\n",
              "      <td>-0.191586</td>\n",
              "      <td>-0.191586</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57.742658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-01 04:00:00</td>\n",
              "      <td>55.402536</td>\n",
              "      <td>42.506345</td>\n",
              "      <td>81.002449</td>\n",
              "      <td>55.402536</td>\n",
              "      <td>55.402536</td>\n",
              "      <td>5.739037</td>\n",
              "      <td>5.739037</td>\n",
              "      <td>5.739037</td>\n",
              "      <td>3.660496</td>\n",
              "      <td>...</td>\n",
              "      <td>2.250546</td>\n",
              "      <td>2.250546</td>\n",
              "      <td>2.250546</td>\n",
              "      <td>-0.172005</td>\n",
              "      <td>-0.172005</td>\n",
              "      <td>-0.172005</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>61.141573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26642</th>\n",
              "      <td>2022-12-28 00:00:00</td>\n",
              "      <td>62.698148</td>\n",
              "      <td>18.183788</td>\n",
              "      <td>98.222626</td>\n",
              "      <td>29.719234</td>\n",
              "      <td>93.759326</td>\n",
              "      <td>-3.631916</td>\n",
              "      <td>-3.631916</td>\n",
              "      <td>-3.631916</td>\n",
              "      <td>-3.085075</td>\n",
              "      <td>...</td>\n",
              "      <td>1.488286</td>\n",
              "      <td>1.488286</td>\n",
              "      <td>1.488286</td>\n",
              "      <td>-2.035127</td>\n",
              "      <td>-2.035127</td>\n",
              "      <td>-2.035127</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>59.066232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26643</th>\n",
              "      <td>2022-12-29 00:00:00</td>\n",
              "      <td>62.709968</td>\n",
              "      <td>19.354455</td>\n",
              "      <td>96.749316</td>\n",
              "      <td>29.543842</td>\n",
              "      <td>93.890325</td>\n",
              "      <td>-3.172654</td>\n",
              "      <td>-3.172654</td>\n",
              "      <td>-3.172654</td>\n",
              "      <td>-3.085075</td>\n",
              "      <td>...</td>\n",
              "      <td>1.531517</td>\n",
              "      <td>1.531517</td>\n",
              "      <td>1.531517</td>\n",
              "      <td>-1.619096</td>\n",
              "      <td>-1.619096</td>\n",
              "      <td>-1.619096</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>59.537314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26644</th>\n",
              "      <td>2022-12-30 00:00:00</td>\n",
              "      <td>62.721788</td>\n",
              "      <td>19.084095</td>\n",
              "      <td>99.130364</td>\n",
              "      <td>29.410255</td>\n",
              "      <td>94.021324</td>\n",
              "      <td>-3.430405</td>\n",
              "      <td>-3.430405</td>\n",
              "      <td>-3.430405</td>\n",
              "      <td>-3.085075</td>\n",
              "      <td>...</td>\n",
              "      <td>0.832271</td>\n",
              "      <td>0.832271</td>\n",
              "      <td>0.832271</td>\n",
              "      <td>-1.177601</td>\n",
              "      <td>-1.177601</td>\n",
              "      <td>-1.177601</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>59.291384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26645</th>\n",
              "      <td>2022-12-31 00:00:00</td>\n",
              "      <td>62.733608</td>\n",
              "      <td>19.013339</td>\n",
              "      <td>94.958848</td>\n",
              "      <td>29.233522</td>\n",
              "      <td>94.152324</td>\n",
              "      <td>-3.841214</td>\n",
              "      <td>-3.841214</td>\n",
              "      <td>-3.841214</td>\n",
              "      <td>-3.085075</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.037581</td>\n",
              "      <td>-0.037581</td>\n",
              "      <td>-0.037581</td>\n",
              "      <td>-0.718557</td>\n",
              "      <td>-0.718557</td>\n",
              "      <td>-0.718557</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>58.892395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26646</th>\n",
              "      <td>2023-01-01 00:00:00</td>\n",
              "      <td>62.745428</td>\n",
              "      <td>15.278281</td>\n",
              "      <td>93.490470</td>\n",
              "      <td>28.994897</td>\n",
              "      <td>94.283323</td>\n",
              "      <td>-7.119926</td>\n",
              "      <td>-7.119926</td>\n",
              "      <td>-7.119926</td>\n",
              "      <td>-3.085075</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.784518</td>\n",
              "      <td>-3.784518</td>\n",
              "      <td>-3.784518</td>\n",
              "      <td>-0.250333</td>\n",
              "      <td>-0.250333</td>\n",
              "      <td>-0.250333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>55.625503</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>26647 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2849a288-78f5-41fc-93b9-999d3e3895eb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2849a288-78f5-41fc-93b9-999d3e3895eb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2849a288-78f5-41fc-93b9-999d3e3895eb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "erroranalytics = m.predict(prophetdf)\n",
        "erroranalytics['BTC'] = prophetdf['y'] # import real value\n",
        "erroranalytics['residuals'] = erroranalytics['BTC'] - erroranalytics['yhat'] # compare predict value to real value\n",
        "erroranalytics['absoluteresiduals'] = abs(erroranalytics['residuals']) # absolute error\n",
        "# Have a glance\n",
        "erroranalytics.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "GlmHYZ1RJBZu",
        "outputId": "b166e6fd-6700-43bc-8f44-94dbe73824ae"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   ds      trend  yhat_lower  yhat_upper  trend_lower  \\\n",
              "0 2019-01-01 00:00:00  55.405466   34.046971   74.598944    55.405466   \n",
              "1 2019-01-01 01:00:00  55.404734   32.185729   73.796696    55.404734   \n",
              "2 2019-01-01 02:00:00  55.404001   34.704972   73.976999    55.404001   \n",
              "3 2019-01-01 03:00:00  55.403269   37.170562   76.423563    55.403269   \n",
              "4 2019-01-01 04:00:00  55.402536   41.656306   82.012850    55.402536   \n",
              "\n",
              "   trend_upper  additive_terms  additive_terms_lower  additive_terms_upper  \\\n",
              "0    55.405466       -1.181216             -1.181216             -1.181216   \n",
              "1    55.404734       -1.981772             -1.981772             -1.981772   \n",
              "2    55.404001       -0.729490             -0.729490             -0.729490   \n",
              "3    55.403269        2.339389              2.339389              2.339389   \n",
              "4    55.402536        5.739037              5.739037              5.739037   \n",
              "\n",
              "      daily  ...    yearly  yearly_lower  yearly_upper  multiplicative_terms  \\\n",
              "0 -3.085075  ... -0.250333     -0.250333     -0.250333                   0.0   \n",
              "1 -3.946791  ... -0.230751     -0.230751     -0.230751                   0.0   \n",
              "2 -2.743576  ... -0.211168     -0.211168     -0.211168                   0.0   \n",
              "3  0.287700  ... -0.191586     -0.191586     -0.191586                   0.0   \n",
              "4  3.660496  ... -0.172005     -0.172005     -0.172005                   0.0   \n",
              "\n",
              "   multiplicative_terms_lower  multiplicative_terms_upper       yhat  BTC  \\\n",
              "0                         0.0                         0.0  54.224250   47   \n",
              "1                         0.0                         0.0  53.422962   33   \n",
              "2                         0.0                         0.0  54.674511   44   \n",
              "3                         0.0                         0.0  57.742658   40   \n",
              "4                         0.0                         0.0  61.141573   42   \n",
              "\n",
              "   residuals  absoluteresiduals  \n",
              "0  -7.224250           7.224250  \n",
              "1 -20.422962          20.422962  \n",
              "2 -10.674511          10.674511  \n",
              "3 -17.742658          17.742658  \n",
              "4 -19.141573          19.141573  \n",
              "\n",
              "[5 rows x 25 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b36a222b-0880-4685-9009-b8dc8344c0d3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>daily</th>\n",
              "      <th>...</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",
              "      <th>BTC</th>\n",
              "      <th>residuals</th>\n",
              "      <th>absoluteresiduals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-01 00:00:00</td>\n",
              "      <td>55.405466</td>\n",
              "      <td>34.046971</td>\n",
              "      <td>74.598944</td>\n",
              "      <td>55.405466</td>\n",
              "      <td>55.405466</td>\n",
              "      <td>-1.181216</td>\n",
              "      <td>-1.181216</td>\n",
              "      <td>-1.181216</td>\n",
              "      <td>-3.085075</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.250333</td>\n",
              "      <td>-0.250333</td>\n",
              "      <td>-0.250333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>54.224250</td>\n",
              "      <td>47</td>\n",
              "      <td>-7.224250</td>\n",
              "      <td>7.224250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-01 01:00:00</td>\n",
              "      <td>55.404734</td>\n",
              "      <td>32.185729</td>\n",
              "      <td>73.796696</td>\n",
              "      <td>55.404734</td>\n",
              "      <td>55.404734</td>\n",
              "      <td>-1.981772</td>\n",
              "      <td>-1.981772</td>\n",
              "      <td>-1.981772</td>\n",
              "      <td>-3.946791</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.230751</td>\n",
              "      <td>-0.230751</td>\n",
              "      <td>-0.230751</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>53.422962</td>\n",
              "      <td>33</td>\n",
              "      <td>-20.422962</td>\n",
              "      <td>20.422962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-01 02:00:00</td>\n",
              "      <td>55.404001</td>\n",
              "      <td>34.704972</td>\n",
              "      <td>73.976999</td>\n",
              "      <td>55.404001</td>\n",
              "      <td>55.404001</td>\n",
              "      <td>-0.729490</td>\n",
              "      <td>-0.729490</td>\n",
              "      <td>-0.729490</td>\n",
              "      <td>-2.743576</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.211168</td>\n",
              "      <td>-0.211168</td>\n",
              "      <td>-0.211168</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>54.674511</td>\n",
              "      <td>44</td>\n",
              "      <td>-10.674511</td>\n",
              "      <td>10.674511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-01 03:00:00</td>\n",
              "      <td>55.403269</td>\n",
              "      <td>37.170562</td>\n",
              "      <td>76.423563</td>\n",
              "      <td>55.403269</td>\n",
              "      <td>55.403269</td>\n",
              "      <td>2.339389</td>\n",
              "      <td>2.339389</td>\n",
              "      <td>2.339389</td>\n",
              "      <td>0.287700</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.191586</td>\n",
              "      <td>-0.191586</td>\n",
              "      <td>-0.191586</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>57.742658</td>\n",
              "      <td>40</td>\n",
              "      <td>-17.742658</td>\n",
              "      <td>17.742658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-01 04:00:00</td>\n",
              "      <td>55.402536</td>\n",
              "      <td>41.656306</td>\n",
              "      <td>82.012850</td>\n",
              "      <td>55.402536</td>\n",
              "      <td>55.402536</td>\n",
              "      <td>5.739037</td>\n",
              "      <td>5.739037</td>\n",
              "      <td>5.739037</td>\n",
              "      <td>3.660496</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.172005</td>\n",
              "      <td>-0.172005</td>\n",
              "      <td>-0.172005</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>61.141573</td>\n",
              "      <td>42</td>\n",
              "      <td>-19.141573</td>\n",
              "      <td>19.141573</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 25 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b36a222b-0880-4685-9009-b8dc8344c0d3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b36a222b-0880-4685-9009-b8dc8344c0d3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b36a222b-0880-4685-9009-b8dc8344c0d3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig1=m.plot(forecast)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "d04zzrOVJtEG",
        "outputId": "b59f4bfd-ea6f-433f-b69a-dbc99f6eca78"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig2 = m.plot_components(forecast)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 873
        },
        "id": "Ye8Hr699J53a",
        "outputId": "78d4ed00-f603-4d9f-d832-e6423db0c1ff"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x864 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "totalerror = erroranalytics['absoluteresiduals'].sum()"
      ],
      "metadata": {
        "id": "dY-UzXnfKB_c"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(totalerror)\n",
        "print(totalerror/len(erroranalytics))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MYGFvOMaKDXT",
        "outputId": "dd86da26-ec51-46f9-e67c-a18dedfa2c3e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "321361.9411775261\n",
            "12.155304530506319\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***HOLIDAYS BASED FORECAST***"
      ],
      "metadata": {
        "id": "At8hS6bnKY5S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "goodholidays = [] # new list for holidays\n",
        "for i in [2019,2020,2021,2022]:\n",
        "  for date, name in sorted(holidays.US(years=i).items()): # take an example of 2014\n",
        "      goodholidays.append(name)\n",
        "      print(date, name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u7x5Qw9QKMyE",
        "outputId": "ca9ecb10-4df2-48cd-e98e-26ea192d34b8"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2019-01-01 New Year's Day\n",
            "2019-01-21 Martin Luther King Jr. Day\n",
            "2019-02-18 Washington's Birthday\n",
            "2019-05-27 Memorial Day\n",
            "2019-07-04 Independence Day\n",
            "2019-09-02 Labor Day\n",
            "2019-10-14 Columbus Day\n",
            "2019-11-11 Veterans Day\n",
            "2019-11-28 Thanksgiving\n",
            "2019-12-25 Christmas Day\n",
            "2020-01-01 New Year's Day\n",
            "2020-01-20 Martin Luther King Jr. Day\n",
            "2020-02-17 Washington's Birthday\n",
            "2020-05-25 Memorial Day\n",
            "2020-07-03 Independence Day (Observed)\n",
            "2020-07-04 Independence Day\n",
            "2020-09-07 Labor Day\n",
            "2020-10-12 Columbus Day\n",
            "2020-11-11 Veterans Day\n",
            "2020-11-26 Thanksgiving\n",
            "2020-12-25 Christmas Day\n",
            "2021-01-01 New Year's Day\n",
            "2021-01-18 Martin Luther King Jr. Day\n",
            "2021-02-15 Washington's Birthday\n",
            "2021-05-31 Memorial Day\n",
            "2021-07-04 Independence Day\n",
            "2021-07-05 Independence Day (Observed)\n",
            "2021-09-06 Labor Day\n",
            "2021-10-11 Columbus Day\n",
            "2021-11-11 Veterans Day\n",
            "2021-11-25 Thanksgiving\n",
            "2021-12-24 Christmas Day (Observed)\n",
            "2021-12-25 Christmas Day\n",
            "2021-12-31 New Year's Day (Observed)\n",
            "2021-12-31 New Year's Day (Observed)\n",
            "2022-01-01 New Year's Day\n",
            "2022-01-17 Martin Luther King Jr. Day\n",
            "2022-02-21 Washington's Birthday\n",
            "2022-05-30 Memorial Day\n",
            "2022-07-04 Independence Day\n",
            "2022-09-05 Labor Day\n",
            "2022-10-10 Columbus Day\n",
            "2022-11-11 Veterans Day\n",
            "2022-11-24 Thanksgiving\n",
            "2022-12-25 Christmas Day\n",
            "2022-12-26 Christmas Day (Observed)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "qyfHJjvQKuBq"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}