Auto_Timeseries.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Auto Timeseries.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMNGH4e+9E6MAUUwXB2JM3/",
      "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/srivatsan88/YouTubeLI/blob/master/Auto_Timeseries.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",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install auto_ts"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CY5G7SE9PLEB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "99df6987-dc8b-4cef-bfa5-0e0dac8c0f14"
      },
      "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": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running Auto Timeseries version: 0.0.19...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",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def parse(x):\n",
        "\treturn datetime.strptime(x, '%m/%d/%Y')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WSoh7Ze0P-jd",
        "colab_type": "code",
        "colab": {}
      },
      "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": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ZIkHt4_sD4u",
        "colab_type": "code",
        "outputId": "8c8aa351-b903-41c0-b64c-799eb93efe83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHXUmVGyJLtF",
        "colab_type": "code",
        "outputId": "3059e6a5-7d7c-4eaa-94b7-de2c09790210",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 6,
      "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",
        "colab_type": "code",
        "outputId": "c4957130-bd62-4051-a2a6-3fe6a3149da8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "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": 7,
      "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",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ec_df = df.set_index('Bill_Date')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BnU-vnefvyt",
        "colab_type": "code",
        "outputId": "c751ffc9-ef8b-481f-d850-cf9fa3b05dc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "ec_df.head()"
      ],
      "execution_count": 9,
      "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>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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0sTtaV86a76l",
        "colab_type": "code",
        "outputId": "d8d7bd77-22fb-4bd0-90d7-baa99714fd8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "ec_data = ec_df['Billed_amount']\n",
        "ec_data.head()"
      ],
      "execution_count": 11,
      "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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "McpB6CtVcjMN",
        "colab_type": "code",
        "outputId": "94fb546d-4118-4b3b-891a-badc0a5b3853",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        }
      },
      "source": [
        "ec_data.plot(grid=True)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f87ca088fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "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",
        "colab_type": "code",
        "outputId": "c173c4e2-3665-4edb-902a-e9486ab18b80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "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": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f87c9f01128>"
            ]
          },
          "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": "DYOMJac8RKUD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "outputId": "9c53ecd8-dd30-4258-f107-68c760b96781"
      },
      "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": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f87c9a2cf28>"
            ]
          },
          "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": "MUMprmLoH2RD",
        "colab_type": "code",
        "outputId": "4694c4aa-56e9-4662-9978-933a5e8f4210",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        }
      },
      "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": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f87c9a97c50>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f87c9964518>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f87c9992908>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f87c993fcf8>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "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",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "final_df = df.copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJHsRdAl5C6I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "outputId": "e2298aa7-52b0-4fd1-916f-ab465f5f60df"
      },
      "source": [
        "final_df.head(10)"
      ],
      "execution_count": 17,
      "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": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04OS1JBzKkCy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "final_df=final_df[['Bill_Date','On_peak','Off_peak','Billed_amount','Billing_days']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZriIFhrF4i7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "outputId": "05f7fe35-195e-4718-dde1-410fb887154a"
      },
      "source": [
        "train = final_df[:50]\n",
        "test = final_df[50:]\n",
        "print(train.shape, test.shape)\n",
        "train.head()"
      ],
      "execution_count": 19,
      "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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HwhAwDnHF4ex",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ts_column = 'Bill_Date'\n",
        "sep = ','\n",
        "target = 'Billed_amount'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xy-GmW13YPx3",
        "colab_type": "text"
      },
      "source": [
        "Use Verbose=1 for more detailed charts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_liVTTyoF4UT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6306fa0b-284b-4a19-f4c8-e8dd9d5567f5"
      },
      "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": 21,
      "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": {
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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:                Thu, 04 Jun 2020   AIC                            473.711\n",
            "Time:                        14:51:11   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",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "d6dc49a2-fa1e-47d7-f783-e1efe6e9980d"
      },
      "source": [
        "ml_dict['FB_Prophet']['forecast']"
      ],
      "execution_count": 22,
      "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": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0PjxYhuDJpy6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "fcc8fa26-cb64-4d48-f132-8d4263a36401"
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 23,
      "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": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HFbFgb7H90ea",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}