09: AutoML_auto_ts_Timeseries_Python.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AutoML_auto_ts_Timeseries_Python.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/AutoML/blob/main/AutoML_auto_ts_Timeseries_Python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oG9AwKBh1PLF",
        "outputId": "24b61d05-2f7e-4384-98d9-99fb0250780a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#install auto_ts(auto time series)\n",
        "!pip install auto_ts"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting auto_ts\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5a/bc/2aefba191eec366ff52bd6ac4b1787d81be3a163dc244d046a81f965875f/auto_ts-0.0.20-py3-none-any.whl (50kB)\n",
            "\r\u001b[K     |██████▌                         | 10kB 26.1MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 20kB 6.3MB/s eta 0:00:01\r\u001b[K     |███████████████████▋            | 30kB 7.4MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▏     | 40kB 8.3MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 51kB 3.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: statsmodels in /usr/local/lib/python3.6/dist-packages (from auto_ts) (0.10.2)\n",
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            "Requirement already satisfied: seaborn in /usr/local/lib/python3.6/dist-packages (from auto_ts) (0.11.0)\n",
            "Collecting pyflux\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/37/e2/ac8ea14d34556e83a9fe9e23ba7b02ca14951849b9637c238ca83d04ac3c/pyflux-0.4.15.tar.gz (1.3MB)\n",
            "\u001b[K     |████████████████████████████████| 1.3MB 13.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from auto_ts) (0.22.2.post1)\n",
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            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->auto_ts) (0.10.0)\n",
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            "Requirement already satisfied: Cython>=0.22 in /usr/local/lib/python3.6/dist-packages (from fbprophet->auto_ts) (0.29.21)\n",
            "Requirement already satisfied: cmdstanpy==0.9.5 in /usr/local/lib/python3.6/dist-packages (from fbprophet->auto_ts) (0.9.5)\n",
            "Requirement already satisfied: pystan>=2.14 in /usr/local/lib/python3.6/dist-packages (from fbprophet->auto_ts) (2.19.1.1)\n",
            "Requirement already satisfied: LunarCalendar>=0.0.9 in /usr/local/lib/python3.6/dist-packages (from fbprophet->auto_ts) (0.0.9)\n",
            "Requirement already satisfied: convertdate>=2.1.2 in /usr/local/lib/python3.6/dist-packages (from fbprophet->auto_ts) (2.2.2)\n",
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            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->auto_ts) (2018.9)\n",
            "Collecting numdifftools\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ab/c0/b0d967160ecc8db52ae34e063937d85e8d386f140ad4826aae2086245a5e/numdifftools-0.9.39-py2.py3-none-any.whl (953kB)\n",
            "\u001b[K     |████████████████████████████████| 962kB 62.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->auto_ts) (0.16.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from patsy>=0.4.0->statsmodels->auto_ts) (1.15.0)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython->auto_ts) (0.2.0)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython->auto_ts) (0.6.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->auto_ts) (0.2.5)\n",
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            "Requirement already satisfied: pymeeus<=1,>=0.3.6 in /usr/local/lib/python3.6/dist-packages (from convertdate>=2.1.2->fbprophet->auto_ts) (0.3.7)\n",
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            "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.6/dist-packages (from nbformat->notebook->jupyter->auto_ts) (2.6.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->jupyter->auto_ts) (0.5.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->jupyter->auto_ts) (20.4)\n",
            "Building wheels for collected packages: pyflux\n",
            "  Building wheel for pyflux (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyflux: filename=pyflux-0.4.15-cp36-cp36m-linux_x86_64.whl size=4656578 sha256=f7792ad2cb3a8605226ee9c6daf5b4f89268f64ccf2ae32725a7929b18cae099\n",
            "  Stored in directory: /root/.cache/pip/wheels/36/24/09/d18fa4f926cc90598d23ebd09fb4b0fd7b3dd1d2f506b952de\n",
            "Successfully built pyflux\n",
            "Installing collected packages: numdifftools, pyflux, auto-ts\n",
            "Successfully installed auto-ts-0.0.20 numdifftools-0.9.39 pyflux-0.4.15\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CY5G7SE9PLEB",
        "outputId": "f14e2507-4e5c-42e4-8bd4-3a9cc9cf01b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime\n",
        "import auto_ts as AT\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (8, 6)\n",
        "mpl.rcParams['axes.grid'] = False\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running Auto Timeseries version: 0.0.20...Call by using:\n",
            "        auto_ts.Auto_Timeseries(traindata, ts_column,\n",
            "                            target, sep,  score_type='rmse', forecast_period=5,\n",
            "                            time_interval='Month', non_seasonal_pdq=None, seasonality=False,\n",
            "                            seasonal_period=12, seasonal_PDQ=None, model_type='stats',\n",
            "                            verbose=1)\n",
            "    To run three models from Stats, ML and FB Prophet, set model_type='best'\n",
            "To remove previous versions, perform 'pip uninstall auto_ts'\n",
            "To get the latest version, perform \"pip install auto_ts --no-cache-dir --ignore-installed\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pc9-9rleb9_Y"
      },
      "source": [
        "def parse(x):\n",
        "\treturn datetime.strptime(x, '%m/%d/%Y')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WSoh7Ze0P-jd"
      },
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/srivatsan88/YouTubeLI/master/dataset/electricity_consumption.csv', parse_dates = ['Bill_Date'],date_parser=parse)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ZIkHt4_sD4u",
        "outputId": "f7051d68-f4bf-4a12-93b2-9e7fd23f96f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Bill_Date</th>\n",
              "      <th>On_peak</th>\n",
              "      <th>Off_peak</th>\n",
              "      <th>Usage_charge</th>\n",
              "      <th>Billed_amount</th>\n",
              "      <th>Billing_days</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>365</td>\n",
              "      <td>1423.5</td>\n",
              "      <td>219.00</td>\n",
              "      <td>247.73</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-02-01</td>\n",
              "      <td>292</td>\n",
              "      <td>1138.8</td>\n",
              "      <td>175.20</td>\n",
              "      <td>234.11</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-03-01</td>\n",
              "      <td>130</td>\n",
              "      <td>507.0</td>\n",
              "      <td>78.00</td>\n",
              "      <td>123.85</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-04-01</td>\n",
              "      <td>117</td>\n",
              "      <td>456.3</td>\n",
              "      <td>70.20</td>\n",
              "      <td>111.22</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-05-01</td>\n",
              "      <td>136</td>\n",
              "      <td>530.4</td>\n",
              "      <td>81.60</td>\n",
              "      <td>118.37</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2016-06-01</td>\n",
              "      <td>63</td>\n",
              "      <td>245.7</td>\n",
              "      <td>37.80</td>\n",
              "      <td>77.81</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2016-07-01</td>\n",
              "      <td>110</td>\n",
              "      <td>429.0</td>\n",
              "      <td>66.00</td>\n",
              "      <td>86.67</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2016-08-01</td>\n",
              "      <td>176</td>\n",
              "      <td>686.4</td>\n",
              "      <td>105.60</td>\n",
              "      <td>122.40</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2016-09-01</td>\n",
              "      <td>357</td>\n",
              "      <td>1392.3</td>\n",
              "      <td>214.20</td>\n",
              "      <td>223.62</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2016-10-01</td>\n",
              "      <td>235</td>\n",
              "      <td>916.5</td>\n",
              "      <td>141.00</td>\n",
              "      <td>152.54</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2016-11-01</td>\n",
              "      <td>193</td>\n",
              "      <td>752.7</td>\n",
              "      <td>115.80</td>\n",
              "      <td>150.54</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2016-12-01</td>\n",
              "      <td>344</td>\n",
              "      <td>1341.6</td>\n",
              "      <td>206.40</td>\n",
              "      <td>244.37</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>347</td>\n",
              "      <td>1353.3</td>\n",
              "      <td>208.20</td>\n",
              "      <td>263.64</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2017-02-01</td>\n",
              "      <td>330</td>\n",
              "      <td>1287.0</td>\n",
              "      <td>198.00</td>\n",
              "      <td>214.32</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2017-03-01</td>\n",
              "      <td>185</td>\n",
              "      <td>1103.0</td>\n",
              "      <td>139.38</td>\n",
              "      <td>151.54</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2017-04-01</td>\n",
              "      <td>179</td>\n",
              "      <td>704.0</td>\n",
              "      <td>109.26</td>\n",
              "      <td>118.79</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2017-05-01</td>\n",
              "      <td>228</td>\n",
              "      <td>829.0</td>\n",
              "      <td>140.89</td>\n",
              "      <td>153.18</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2017-06-01</td>\n",
              "      <td>359</td>\n",
              "      <td>1067.0</td>\n",
              "      <td>195.07</td>\n",
              "      <td>212.08</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2017-07-01</td>\n",
              "      <td>468</td>\n",
              "      <td>1533.0</td>\n",
              "      <td>258.64</td>\n",
              "      <td>281.19</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2017-08-01</td>\n",
              "      <td>400</td>\n",
              "      <td>1252.0</td>\n",
              "      <td>218.87</td>\n",
              "      <td>237.97</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2017-09-01</td>\n",
              "      <td>432</td>\n",
              "      <td>1447.0</td>\n",
              "      <td>261.50</td>\n",
              "      <td>284.30</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2017-10-01</td>\n",
              "      <td>253</td>\n",
              "      <td>777.0</td>\n",
              "      <td>158.83</td>\n",
              "      <td>172.69</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2017-11-01</td>\n",
              "      <td>165</td>\n",
              "      <td>521.0</td>\n",
              "      <td>102.57</td>\n",
              "      <td>111.51</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2017-12-01</td>\n",
              "      <td>186</td>\n",
              "      <td>912.0</td>\n",
              "      <td>139.27</td>\n",
              "      <td>151.42</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>316</td>\n",
              "      <td>1869.0</td>\n",
              "      <td>245.88</td>\n",
              "      <td>267.32</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2018-02-01</td>\n",
              "      <td>256</td>\n",
              "      <td>1229.0</td>\n",
              "      <td>185.20</td>\n",
              "      <td>201.35</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2018-03-01</td>\n",
              "      <td>266</td>\n",
              "      <td>1337.0</td>\n",
              "      <td>194.50</td>\n",
              "      <td>202.91</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2018-04-01</td>\n",
              "      <td>141</td>\n",
              "      <td>812.0</td>\n",
              "      <td>143.44</td>\n",
              "      <td>150.86</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2018-05-01</td>\n",
              "      <td>167</td>\n",
              "      <td>791.0</td>\n",
              "      <td>154.70</td>\n",
              "      <td>163.07</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2018-06-01</td>\n",
              "      <td>257</td>\n",
              "      <td>1051.0</td>\n",
              "      <td>208.55</td>\n",
              "      <td>219.76</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2018-07-01</td>\n",
              "      <td>295</td>\n",
              "      <td>1482.0</td>\n",
              "      <td>269.93</td>\n",
              "      <td>283.98</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2018-08-01</td>\n",
              "      <td>330</td>\n",
              "      <td>1470.0</td>\n",
              "      <td>275.71</td>\n",
              "      <td>290.15</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2018-09-01</td>\n",
              "      <td>261</td>\n",
              "      <td>1375.0</td>\n",
              "      <td>245.48</td>\n",
              "      <td>257.96</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2018-10-01</td>\n",
              "      <td>366</td>\n",
              "      <td>1705.0</td>\n",
              "      <td>311.34</td>\n",
              "      <td>327.19</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2018-11-01</td>\n",
              "      <td>245</td>\n",
              "      <td>1036.0</td>\n",
              "      <td>184.40</td>\n",
              "      <td>193.49</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2018-12-01</td>\n",
              "      <td>125</td>\n",
              "      <td>899.0</td>\n",
              "      <td>145.26</td>\n",
              "      <td>152.34</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>151</td>\n",
              "      <td>1257.0</td>\n",
              "      <td>187.91</td>\n",
              "      <td>196.62</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2019-02-01</td>\n",
              "      <td>121</td>\n",
              "      <td>1281.0</td>\n",
              "      <td>181.85</td>\n",
              "      <td>190.07</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2019-03-01</td>\n",
              "      <td>128</td>\n",
              "      <td>1351.0</td>\n",
              "      <td>190.04</td>\n",
              "      <td>198.57</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2019-04-01</td>\n",
              "      <td>95</td>\n",
              "      <td>683.0</td>\n",
              "      <td>111.62</td>\n",
              "      <td>115.15</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2019-05-01</td>\n",
              "      <td>162</td>\n",
              "      <td>783.0</td>\n",
              "      <td>147.83</td>\n",
              "      <td>153.18</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>2019-06-01</td>\n",
              "      <td>131</td>\n",
              "      <td>722.0</td>\n",
              "      <td>127.51</td>\n",
              "      <td>138.53</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>2019-07-01</td>\n",
              "      <td>276</td>\n",
              "      <td>1254.0</td>\n",
              "      <td>219.52</td>\n",
              "      <td>238.48</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>2019-08-01</td>\n",
              "      <td>376</td>\n",
              "      <td>1636.0</td>\n",
              "      <td>284.90</td>\n",
              "      <td>309.65</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>2019-09-01</td>\n",
              "      <td>501</td>\n",
              "      <td>2139.0</td>\n",
              "      <td>371.61</td>\n",
              "      <td>403.89</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>2019-10-01</td>\n",
              "      <td>279</td>\n",
              "      <td>1309.0</td>\n",
              "      <td>224.52</td>\n",
              "      <td>244.03</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>2019-11-01</td>\n",
              "      <td>171</td>\n",
              "      <td>843.0</td>\n",
              "      <td>133.84</td>\n",
              "      <td>145.46</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>2019-12-01</td>\n",
              "      <td>99</td>\n",
              "      <td>927.0</td>\n",
              "      <td>128.12</td>\n",
              "      <td>139.26</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>2020-01-01</td>\n",
              "      <td>211</td>\n",
              "      <td>1761.0</td>\n",
              "      <td>231.71</td>\n",
              "      <td>251.85</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>2020-02-01</td>\n",
              "      <td>190</td>\n",
              "      <td>1635.0</td>\n",
              "      <td>210.86</td>\n",
              "      <td>229.18</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>2020-03-01</td>\n",
              "      <td>151</td>\n",
              "      <td>1179.0</td>\n",
              "      <td>156.56</td>\n",
              "      <td>170.16</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>75</td>\n",
              "      <td>625.0</td>\n",
              "      <td>87.27</td>\n",
              "      <td>94.85</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>2020-05-01</td>\n",
              "      <td>147</td>\n",
              "      <td>686.0</td>\n",
              "      <td>124.39</td>\n",
              "      <td>135.19</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Bill_Date  On_peak  Off_peak  Usage_charge  Billed_amount  Billing_days\n",
              "0  2016-01-01      365    1423.5        219.00         247.73            31\n",
              "1  2016-02-01      292    1138.8        175.20         234.11            31\n",
              "2  2016-03-01      130     507.0         78.00         123.85            29\n",
              "3  2016-04-01      117     456.3         70.20         111.22            29\n",
              "4  2016-05-01      136     530.4         81.60         118.37            29\n",
              "5  2016-06-01       63     245.7         37.80          77.81            32\n",
              "6  2016-07-01      110     429.0         66.00          86.67            30\n",
              "7  2016-08-01      176     686.4        105.60         122.40            29\n",
              "8  2016-09-01      357    1392.3        214.20         223.62            33\n",
              "9  2016-10-01      235     916.5        141.00         152.54            29\n",
              "10 2016-11-01      193     752.7        115.80         150.54            32\n",
              "11 2016-12-01      344    1341.6        206.40         244.37            31\n",
              "12 2017-01-01      347    1353.3        208.20         263.64            33\n",
              "13 2017-02-01      330    1287.0        198.00         214.32            27\n",
              "14 2017-03-01      185    1103.0        139.38         151.54            31\n",
              "15 2017-04-01      179     704.0        109.26         118.79            29\n",
              "16 2017-05-01      228     829.0        140.89         153.18            31\n",
              "17 2017-06-01      359    1067.0        195.07         212.08            31\n",
              "18 2017-07-01      468    1533.0        258.64         281.19            32\n",
              "19 2017-08-01      400    1252.0        218.87         237.97            30\n",
              "20 2017-09-01      432    1447.0        261.50         284.30            30\n",
              "21 2017-10-01      253     777.0        158.83         172.69            31\n",
              "22 2017-11-01      165     521.0        102.57         111.51            29\n",
              "23 2017-12-01      186     912.0        139.27         151.42            31\n",
              "24 2018-01-01      316    1869.0        245.88         267.32            33\n",
              "25 2018-02-01      256    1229.0        185.20         201.35            29\n",
              "26 2018-03-01      266    1337.0        194.50         202.91            26\n",
              "27 2018-04-01      141     812.0        143.44         150.86            34\n",
              "28 2018-05-01      167     791.0        154.70         163.07            29\n",
              "29 2018-06-01      257    1051.0        208.55         219.76            30\n",
              "30 2018-07-01      295    1482.0        269.93         283.98            33\n",
              "31 2018-08-01      330    1470.0        275.71         290.15            29\n",
              "32 2018-09-01      261    1375.0        245.48         257.96            26\n",
              "33 2018-10-01      366    1705.0        311.34         327.19            25\n",
              "34 2018-11-01      245    1036.0        184.40         193.49            30\n",
              "35 2018-12-01      125     899.0        145.26         152.34            32\n",
              "36 2019-01-01      151    1257.0        187.91         196.62            31\n",
              "37 2019-02-01      121    1281.0        181.85         190.07            31\n",
              "38 2019-03-01      128    1351.0        190.04         198.57            27\n",
              "39 2019-04-01       95     683.0        111.62         115.15            31\n",
              "40 2019-05-01      162     783.0        147.83         153.18            29\n",
              "41 2019-06-01      131     722.0        127.51         138.53            31\n",
              "42 2019-07-01      276    1254.0        219.52         238.48            31\n",
              "43 2019-08-01      376    1636.0        284.90         309.65            30\n",
              "44 2019-09-01      501    2139.0        371.61         403.89            34\n",
              "45 2019-10-01      279    1309.0        224.52         244.03            27\n",
              "46 2019-11-01      171     843.0        133.84         145.46            31\n",
              "47 2019-12-01       99     927.0        128.12         139.26            32\n",
              "48 2020-01-01      211    1761.0        231.71         251.85            31\n",
              "49 2020-02-01      190    1635.0        210.86         229.18            32\n",
              "50 2020-03-01      151    1179.0        156.56         170.16            29\n",
              "51 2020-04-01       75     625.0         87.27          94.85            29\n",
              "52 2020-05-01      147     686.0        124.39         135.19            29"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHXUmVGyJLtF",
        "outputId": "1597a397-79cf-41fd-8614-947309778fba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 53 entries, 0 to 52\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   Bill_Date      53 non-null     datetime64[ns]\n",
            " 1   On_peak        53 non-null     int64         \n",
            " 2   Off_peak       53 non-null     float64       \n",
            " 3   Usage_charge   53 non-null     float64       \n",
            " 4   Billed_amount  53 non-null     float64       \n",
            " 5   Billing_days   53 non-null     int64         \n",
            "dtypes: datetime64[ns](1), float64(3), int64(2)\n",
            "memory usage: 2.6 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ekRbR-YqwEtA",
        "outputId": "277648f7-9af2-4b08-d198-b8a99e8e2dc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        }
      },
      "source": [
        "print (\"Rows     : \" ,df.shape[0])\n",
        "print (\"Columns  : \" ,df.shape[1])\n",
        "print (\"\\nFeatures : \\n\" ,df.columns.tolist())\n",
        "print (\"\\nMissing values :  \", df.isnull().any())\n",
        "print (\"\\nUnique values :  \\n\",df.nunique())"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rows     :  53\n",
            "Columns  :  6\n",
            "\n",
            "Features : \n",
            " ['Bill_Date', 'On_peak', 'Off_peak', 'Usage_charge', 'Billed_amount', 'Billing_days']\n",
            "\n",
            "Missing values :   Bill_Date        False\n",
            "On_peak          False\n",
            "Off_peak         False\n",
            "Usage_charge     False\n",
            "Billed_amount    False\n",
            "Billing_days     False\n",
            "dtype: bool\n",
            "\n",
            "Unique values :  \n",
            " Bill_Date        53\n",
            "On_peak          51\n",
            "Off_peak         53\n",
            "Usage_charge     53\n",
            "Billed_amount    52\n",
            "Billing_days      9\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1oKlNM0Vfogp"
      },
      "source": [
        "ec_df = df.set_index('Bill_Date')"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BnU-vnefvyt",
        "outputId": "7e8e6179-4f50-425e-ca3e-a33df458579d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "ec_df.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>On_peak</th>\n",
              "      <th>Off_peak</th>\n",
              "      <th>Usage_charge</th>\n",
              "      <th>Billed_amount</th>\n",
              "      <th>Billing_days</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bill_Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-01-01</th>\n",
              "      <td>365</td>\n",
              "      <td>1423.5</td>\n",
              "      <td>219.0</td>\n",
              "      <td>247.73</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-01</th>\n",
              "      <td>292</td>\n",
              "      <td>1138.8</td>\n",
              "      <td>175.2</td>\n",
              "      <td>234.11</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-03-01</th>\n",
              "      <td>130</td>\n",
              "      <td>507.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>123.85</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-04-01</th>\n",
              "      <td>117</td>\n",
              "      <td>456.3</td>\n",
              "      <td>70.2</td>\n",
              "      <td>111.22</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-05-01</th>\n",
              "      <td>136</td>\n",
              "      <td>530.4</td>\n",
              "      <td>81.6</td>\n",
              "      <td>118.37</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            On_peak  Off_peak  Usage_charge  Billed_amount  Billing_days\n",
              "Bill_Date                                                               \n",
              "2016-01-01      365    1423.5         219.0         247.73            31\n",
              "2016-02-01      292    1138.8         175.2         234.11            31\n",
              "2016-03-01      130     507.0          78.0         123.85            29\n",
              "2016-04-01      117     456.3          70.2         111.22            29\n",
              "2016-05-01      136     530.4          81.6         118.37            29"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0sTtaV86a76l",
        "outputId": "584ef177-6890-4c98-b4b4-100314356ae3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "ec_data = ec_df['Billed_amount']\n",
        "ec_data.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bill_Date\n",
              "2016-01-01    247.73\n",
              "2016-02-01    234.11\n",
              "2016-03-01    123.85\n",
              "2016-04-01    111.22\n",
              "2016-05-01    118.37\n",
              "Name: Billed_amount, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "McpB6CtVcjMN",
        "outputId": "bf939b11-162e-479d-90c9-f87202366ae3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        }
      },
      "source": [
        "ec_data.plot(grid=True)\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1b22dc6d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBCZn1zUPWRJ",
        "outputId": "66fe1028-fc44-4f06-cf64-0f514f882821",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "ec_df_2019=ec_df.loc['2019']\n",
        "ec_data_2019=ec_df_2019['Billed_amount']\n",
        "ec_data_2019.plot(grid=True)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1b21cdc50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYOMJac8RKUD",
        "outputId": "4c24a640-5bf4-441e-a62d-53d557986ff4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "source": [
        "ec_df_2019=ec_df.loc['2019']\n",
        "ec_data_2019=ec_df_2019['Billed_amount']\n",
        "ec_data_2019.plot.bar()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd1b1d7bda0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUMprmLoH2RD",
        "outputId": "68bd43fe-56b7-4634-f39f-8efd9e53a603",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        }
      },
      "source": [
        "ec_df_2019=ec_df.loc['2019']\n",
        "ec_data_2019=ec_df_2019[['Billed_amount','On_peak','Off_peak','Billing_days']]\n",
        "ec_data_2019.plot(subplots=True)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fd1b1c869b0>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fd1b1cb3cf8>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fd1b1c73128>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fd1b1c24518>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VsRF5qBxoWnq"
      },
      "source": [
        "final_df = df.copy()"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJHsRdAl5C6I",
        "outputId": "bee906bc-2ac7-4298-a337-79398b5d1bbb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "final_df.head(10)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Bill_Date</th>\n",
              "      <th>On_peak</th>\n",
              "      <th>Off_peak</th>\n",
              "      <th>Usage_charge</th>\n",
              "      <th>Billed_amount</th>\n",
              "      <th>Billing_days</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>365</td>\n",
              "      <td>1423.5</td>\n",
              "      <td>219.0</td>\n",
              "      <td>247.73</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-02-01</td>\n",
              "      <td>292</td>\n",
              "      <td>1138.8</td>\n",
              "      <td>175.2</td>\n",
              "      <td>234.11</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-03-01</td>\n",
              "      <td>130</td>\n",
              "      <td>507.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>123.85</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-04-01</td>\n",
              "      <td>117</td>\n",
              "      <td>456.3</td>\n",
              "      <td>70.2</td>\n",
              "      <td>111.22</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-05-01</td>\n",
              "      <td>136</td>\n",
              "      <td>530.4</td>\n",
              "      <td>81.6</td>\n",
              "      <td>118.37</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2016-06-01</td>\n",
              "      <td>63</td>\n",
              "      <td>245.7</td>\n",
              "      <td>37.8</td>\n",
              "      <td>77.81</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2016-07-01</td>\n",
              "      <td>110</td>\n",
              "      <td>429.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>86.67</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2016-08-01</td>\n",
              "      <td>176</td>\n",
              "      <td>686.4</td>\n",
              "      <td>105.6</td>\n",
              "      <td>122.40</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2016-09-01</td>\n",
              "      <td>357</td>\n",
              "      <td>1392.3</td>\n",
              "      <td>214.2</td>\n",
              "      <td>223.62</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2016-10-01</td>\n",
              "      <td>235</td>\n",
              "      <td>916.5</td>\n",
              "      <td>141.0</td>\n",
              "      <td>152.54</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Bill_Date  On_peak  Off_peak  Usage_charge  Billed_amount  Billing_days\n",
              "0 2016-01-01      365    1423.5         219.0         247.73            31\n",
              "1 2016-02-01      292    1138.8         175.2         234.11            31\n",
              "2 2016-03-01      130     507.0          78.0         123.85            29\n",
              "3 2016-04-01      117     456.3          70.2         111.22            29\n",
              "4 2016-05-01      136     530.4          81.6         118.37            29\n",
              "5 2016-06-01       63     245.7          37.8          77.81            32\n",
              "6 2016-07-01      110     429.0          66.0          86.67            30\n",
              "7 2016-08-01      176     686.4         105.6         122.40            29\n",
              "8 2016-09-01      357    1392.3         214.2         223.62            33\n",
              "9 2016-10-01      235     916.5         141.0         152.54            29"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04OS1JBzKkCy"
      },
      "source": [
        "final_df=final_df[['Bill_Date','On_peak','Off_peak','Billed_amount','Billing_days']]"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZriIFhrF4i7",
        "outputId": "625d2d96-cc0e-4832-ac74-0b004dfb72ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "train = final_df[:50]\n",
        "test = final_df[50:]\n",
        "print(train.shape, test.shape)\n",
        "train.head()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(50, 5) (3, 5)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>Bill_Date</th>\n",
              "      <th>On_peak</th>\n",
              "      <th>Off_peak</th>\n",
              "      <th>Billed_amount</th>\n",
              "      <th>Billing_days</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>365</td>\n",
              "      <td>1423.5</td>\n",
              "      <td>247.73</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-02-01</td>\n",
              "      <td>292</td>\n",
              "      <td>1138.8</td>\n",
              "      <td>234.11</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-03-01</td>\n",
              "      <td>130</td>\n",
              "      <td>507.0</td>\n",
              "      <td>123.85</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-04-01</td>\n",
              "      <td>117</td>\n",
              "      <td>456.3</td>\n",
              "      <td>111.22</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-05-01</td>\n",
              "      <td>136</td>\n",
              "      <td>530.4</td>\n",
              "      <td>118.37</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Bill_Date  On_peak  Off_peak  Billed_amount  Billing_days\n",
              "0 2016-01-01      365    1423.5         247.73            31\n",
              "1 2016-02-01      292    1138.8         234.11            31\n",
              "2 2016-03-01      130     507.0         123.85            29\n",
              "3 2016-04-01      117     456.3         111.22            29\n",
              "4 2016-05-01      136     530.4         118.37            29"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HwhAwDnHF4ex"
      },
      "source": [
        "ts_column = 'Bill_Date'\n",
        "sep = ','\n",
        "target = 'Billed_amount'"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xy-GmW13YPx3"
      },
      "source": [
        "Use Verbose=1 for more detailed charts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_liVTTyoF4UT",
        "outputId": "13bbfdf6-8284-487d-a583-f2c6d1b30307",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "ml_dict = AT.Auto_Timeseries(train, ts_column,\n",
        "                            target, sep,  score_type='rmse', forecast_period=6,\n",
        "                            time_interval='Months', non_seasonal_pdq=None, seasonality=True,\n",
        "                            seasonal_period=12,seasonal_PDQ=None, model_type='best',\n",
        "                            verbose=2)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input is data frame. Performing Time Series Analysis\n",
            "    Dataframe loaded successfully. Shape of data set = (50, 4)\n",
            "    Target variable = Billed_amount\n",
            "Time Interval is given as Months\n",
            "\u001b[1mWARNING: Running best models will take time... Be Patient...\u001b[0m\n",
            "\u001b[1m\n",
            "Running Facebook Prophet Model...\u001b[0m\n",
            "Preparing Time Series data for FB Prophet: sample row before\n",
            "             Billed_amount  On_peak  Off_peak  Billing_days\n",
            "Bill_Date                                                 \n",
            "2016-01-01         247.73      365    1423.5            31\n",
            "Time Series data: sample row after transformation\n",
            "           ds       y  On_peak  Off_peak  Billing_days\n",
            "0 2016-01-01  247.73      365    1423.5            31\n",
            "    Fit-Predict data (shape=(50, 2)) with Confidence Interval = 0.95...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Building Forecast dataframe. Forecast Period = 6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "    RMSE = 39.91\n",
            "    Std Deviation of Originals = 68.88\n",
            "    Normalized RMSE = 58%\n",
            "\u001b[1m\n",
            "Running PyFlux Model...\u001b[0m\n",
            "Cycling through various (p,d,q) parameters\n",
            "\n",
            "Best Params Selected (based on rmse): (3, 1, 3)\n",
            "Dynamic 6-period Forecasts:\n",
            "    RMSE = 113.93\n",
            "    Std Deviation of Originals = 64.13\n",
            "    Normalized RMSE = 178%\n",
            "\u001b[1m\n",
            "Running Non Seasonal ARIMA Model...\u001b[0m\n",
            "\n",
            "Differencing = 0\n",
            " Iteration 0 completed...\n",
            "    Best AR order p = 0, MA order q = 3, Interim metric = 480.447\n",
            "\n",
            "Differencing = 1\n",
            "    Best AR order p = 0, MA order q = 3, Interim metric = 473.711\n",
            "\n",
            "Best model is: Non Seasonal ARIMA(0,1,3), aic = 473.711\n",
            "####    Fitting best model for full data set now. Will take time... ######\n",
            "Static Forecasts:\n",
            "Dynamic predictions erroring but continuing...\n",
            "\n",
            "Dynamic 6-period Forecasts:\n",
            "                             ARIMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:        D.Billed_amount   No. Observations:                   43\n",
            "Model:                 ARIMA(0, 1, 3)   Log Likelihood                -231.856\n",
            "Method:                           mle   S.D. of innovations             51.351\n",
            "Date:                Tue, 20 Oct 2020   AIC                            473.711\n",
            "Time:                        10:00:13   BIC                            482.517\n",
            "Sample:                    02-01-2016   HQIC                           476.959\n",
            "                         - 08-01-2019                                         \n",
            "=========================================================================================\n",
            "                            coef    std err          z      P>|z|      [0.025      0.975]\n",
            "-----------------------------------------------------------------------------------------\n",
            "const                     1.4869      1.165      1.276      0.209      -0.797       3.770\n",
            "ma.L1.D.Billed_amount    -0.3021      0.173     -1.745      0.089      -0.641       0.037\n",
            "ma.L2.D.Billed_amount    -0.4015      0.136     -2.953      0.005      -0.668      -0.135\n",
            "ma.L3.D.Billed_amount    -0.2962      0.171     -1.728      0.092      -0.632       0.040\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "MA.1            1.0001           -0.0000j            1.0001           -0.0000\n",
            "MA.2           -1.1778           -1.4101j            1.8373           -0.3608\n",
            "MA.3           -1.1778           +1.4101j            1.8373            0.3608\n",
            "-----------------------------------------------------------------------------\n",
            "    RMSE = 70.15\n",
            "    Std Deviation of Originals = 64.13\n",
            "    Normalized RMSE = 109%\n",
            "\u001b[1m\n",
            "Running Seasonal SARIMAX Model...\u001b[0m\n",
            "\u001b[1mBuilding a Seasonal Model...\u001b[0m\n",
            "\u001b[1m\n",
            "    Finding best Non-Seasonal pdq Parameters:\u001b[0m\n",
            "\n",
            "Differencing = 0\n",
            "    Iteration 0 completed...\n",
            "    Best AR order p = 1, MA order q = 3, Interim metric = 436.948\n",
            "\n",
            "Differencing = 1\n",
            "    D = 1 results in an empty ARMA set. Setting Seasonality to False since model might overfit\n",
            "\u001b[1m\n",
            "    Finding best Seasonal PDQ Model Parameters:\u001b[0m\n",
            "\n",
            "Differencing = 0\n",
            "    D = 0 results in an empty ARMA set. Setting Seasonality to False since model might overfit\n",
            "\n",
            "Differencing = 1\n",
            "    Iteration 0 completed...\n",
            "    Best AR order p = 0, MA order q = 3, Interim metric = 431.652\n",
            "\n",
            "Best model is a Seasonal SARIMAX(1,0,3)*(0,1,3,12), aic = 431.652\n",
            "\u001b[1mFitting best SARIMAX model for full data set\u001b[0m\n",
            "Error: Getting Singular Matrix. Please try using other PDQ parameters or turn off Seasonality\n",
            "\u001b[1m\n",
            "Running VAR Model...\u001b[0m\n",
            "    Shifting 3 predictors by 1 to align prior predictor values with current target values...\n",
            "\n",
            "Additional Variable in VAR model = On_peak\n",
            " Iteration 1 completed\n",
            " Iteration 2 completed\n",
            " Iteration 3 completed\n",
            " Iteration 4 completed\n",
            " Iteration 5 completed\n",
            " Iteration 6 completed\n",
            " Iteration 7 completed\n",
            " Iteration 8 completed\n",
            " Iteration 9 completed\n",
            " Iteration 10 completed\n",
            " Iteration 11 completed\n",
            " Iteration 12 completed\n",
            " Iteration 13 completed\n",
            " Iteration 14 completed\n",
            " Iteration 15 completed\n",
            " Iteration 16 completed\n",
            "    Best AR order p = 2, MA order q = 0, Interim metric = 907.037\n",
            "\n",
            "Additional Variable in VAR model = Off_peak\n",
            " Iteration 17 completed\n",
            " Iteration 18 completed\n",
            " Iteration 19 completed\n",
            " Iteration 20 completed\n",
            " Iteration 21 completed\n",
            " Iteration 22 completed\n",
            " Iteration 23 completed\n",
            " Iteration 24 completed\n",
            " Iteration 25 completed\n",
            " Iteration 26 completed\n",
            " Iteration 27 completed\n",
            " Iteration 28 completed\n",
            " Iteration 29 completed\n",
            " Iteration 30 completed\n",
            " Iteration 31 completed\n",
            " Iteration 32 completed\n",
            "    Best AR order p = 2, MA order q = 0, Interim metric = 1012.235\n",
            "\n",
            "Additional Variable in VAR model = Billing_days\n",
            " Iteration 33 completed\n",
            " Iteration 34 completed\n",
            " Iteration 35 completed\n",
            " Iteration 36 completed\n",
            " Iteration 37 completed\n",
            " Iteration 38 completed\n",
            " Iteration 39 completed\n",
            " Iteration 40 completed\n",
            " Iteration 41 completed\n",
            " Iteration 42 completed\n",
            " Iteration 43 completed\n",
            " Iteration 44 completed\n",
            " Iteration 45 completed\n",
            " Iteration 46 completed\n",
            " Iteration 47 completed\n",
            " Iteration 48 completed\n",
            "    Best AR order p = 0, MA order q = 3, Interim metric = 656.069\n",
            "Best variable selected for VAR: Billing_days\n",
            "    RMSE = 66.57\n",
            "    Std Deviation of Originals = 64.33\n",
            "    Normalized RMSE = 103%\n",
            "\u001b[1m\n",
            "Running Machine Learning Models...\u001b[0m\n",
            "    Shifting 3 predictors by lag=5 to align prior predictor with current target...\n",
            "Running multiple models...\n",
            "Model Results:\n",
            "    RMSE = 82.80\n",
            "    Std Deviation of Originals = 60.75\n",
            "    Normalized RMSE = 136%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x1440 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "Best Model is:\u001b[0m\n",
            "    FB_Prophet\n",
            "    Best Model Forecasts: [212.7912669  201.479645   104.6525523  113.84473249 145.46329079\n",
            " 165.98211532 195.68268584 188.80549366 248.56798017 178.08926727\n",
            " 140.35609375 203.09455071 227.62423449 165.16176902 148.16803543\n",
            " 121.25359542 146.69235633 163.19266535 213.13869827 222.69884433\n",
            " 277.85372891 209.10096487 147.39210372 181.99981746 244.22247547\n",
            " 199.20915277 191.13093461 127.84041073 147.66982156 160.748907\n",
            " 231.31118264 256.95702294 307.00622509 239.48253599 153.61965283\n",
            " 161.17273136 261.81269184 233.86047307 233.50703388 133.61321645\n",
            " 148.42398338 158.67910471 250.19711359 291.5532215  335.99982018\n",
            " 269.2190431  159.05898836 140.66143846 280.3998105  269.08812391\n",
            " 275.26397235 138.58252933 148.98467282 157.01204929 269.79349077\n",
            " 326.46115931]\n",
            "    Best Model Score: 39.91\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9VdEjmw5CwA",
        "outputId": "b9dbfaf5-5f1c-44dc-bbb4-7e91e876189f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "ml_dict['FB_Prophet']['forecast']"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([212.7912669 , 201.479645  , 104.6525523 , 113.84473249,\n",
              "       145.46329079, 165.98211532, 195.68268584, 188.80549366,\n",
              "       248.56798017, 178.08926727, 140.35609375, 203.09455071,\n",
              "       227.62423449, 165.16176902, 148.16803543, 121.25359542,\n",
              "       146.69235633, 163.19266535, 213.13869827, 222.69884433,\n",
              "       277.85372891, 209.10096487, 147.39210372, 181.99981746,\n",
              "       244.22247547, 199.20915277, 191.13093461, 127.84041073,\n",
              "       147.66982156, 160.748907  , 231.31118264, 256.95702294,\n",
              "       307.00622509, 239.48253599, 153.61965283, 161.17273136,\n",
              "       261.81269184, 233.86047307, 233.50703388, 133.61321645,\n",
              "       148.42398338, 158.67910471, 250.19711359, 291.5532215 ,\n",
              "       335.99982018, 269.2190431 , 159.05898836, 140.66143846,\n",
              "       280.3998105 , 269.08812391, 275.26397235, 138.58252933,\n",
              "       148.98467282, 157.01204929, 269.79349077, 326.46115931])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0PjxYhuDJpy6",
        "outputId": "90c87c6b-b723-4d90-b223-dae041777ee8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Bill_Date</th>\n",
              "      <th>On_peak</th>\n",
              "      <th>Off_peak</th>\n",
              "      <th>Billed_amount</th>\n",
              "      <th>Billing_days</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>2020-03-01</td>\n",
              "      <td>151</td>\n",
              "      <td>1179.0</td>\n",
              "      <td>170.16</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>75</td>\n",
              "      <td>625.0</td>\n",
              "      <td>94.85</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>2020-05-01</td>\n",
              "      <td>147</td>\n",
              "      <td>686.0</td>\n",
              "      <td>135.19</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Bill_Date  On_peak  Off_peak  Billed_amount  Billing_days\n",
              "50 2020-03-01      151    1179.0         170.16            29\n",
              "51 2020-04-01       75     625.0          94.85            29\n",
              "52 2020-05-01      147     686.0         135.19            29"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HFbFgb7H90ea"
      },
      "source": [
        ""
      ],
      "execution_count": 24,
      "outputs": []
    }
  ]
}