Time Series Forecasting with NeuralProphet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b250c2dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install neuralprophet\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#import pickle  #to save our trained model as an object that can be resued by other coders\n",
    "#from neuralprophet import NeuralProphet\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c4ad70a6",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2008-12-06</td>\n",
       "      <td>Albury</td>\n",
       "      <td>14.6</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>56.0</td>\n",
       "      <td>W</td>\n",
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       "      <td>55.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1009.2</td>\n",
       "      <td>1005.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.6</td>\n",
       "      <td>28.9</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2008-12-07</td>\n",
       "      <td>Albury</td>\n",
       "      <td>14.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>50.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>...</td>\n",
       "      <td>49.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1009.6</td>\n",
       "      <td>1008.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008-12-08</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.7</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>35.0</td>\n",
       "      <td>SSE</td>\n",
       "      <td>...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1013.4</td>\n",
       "      <td>1010.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008-12-09</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.7</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NNW</td>\n",
       "      <td>80.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1008.9</td>\n",
       "      <td>1003.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.3</td>\n",
       "      <td>30.2</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2008-12-10</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.1</td>\n",
       "      <td>30.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>28.0</td>\n",
       "      <td>S</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>1005.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.1</td>\n",
       "      <td>28.2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2008-12-11</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SSE</td>\n",
       "      <td>...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1011.8</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.4</td>\n",
       "      <td>28.8</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2008-12-12</td>\n",
       "      <td>Albury</td>\n",
       "      <td>15.9</td>\n",
       "      <td>21.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NNE</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>...</td>\n",
       "      <td>89.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1010.5</td>\n",
       "      <td>1004.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2008-12-13</td>\n",
       "      <td>Albury</td>\n",
       "      <td>15.9</td>\n",
       "      <td>18.6</td>\n",
       "      <td>15.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>61.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>...</td>\n",
       "      <td>76.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>994.3</td>\n",
       "      <td>993.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>15.8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2008-12-14</td>\n",
       "      <td>Albury</td>\n",
       "      <td>12.6</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>65.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1001.2</td>\n",
       "      <td>1001.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.8</td>\n",
       "      <td>19.8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2008-12-15</td>\n",
       "      <td>Albury</td>\n",
       "      <td>8.4</td>\n",
       "      <td>24.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>...</td>\n",
       "      <td>57.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1009.7</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.9</td>\n",
       "      <td>23.5</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2008-12-16</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.8</td>\n",
       "      <td>27.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1013.4</td>\n",
       "      <td>1010.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.3</td>\n",
       "      <td>26.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2008-12-17</td>\n",
       "      <td>Albury</td>\n",
       "      <td>14.1</td>\n",
       "      <td>20.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ENE</td>\n",
       "      <td>22.0</td>\n",
       "      <td>SSW</td>\n",
       "      <td>...</td>\n",
       "      <td>69.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1012.2</td>\n",
       "      <td>1010.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2008-12-18</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.5</td>\n",
       "      <td>22.9</td>\n",
       "      <td>16.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>63.0</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>80.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1005.8</td>\n",
       "      <td>1002.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2008-12-19</td>\n",
       "      <td>Albury</td>\n",
       "      <td>11.2</td>\n",
       "      <td>22.5</td>\n",
       "      <td>10.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SSE</td>\n",
       "      <td>43.0</td>\n",
       "      <td>WSW</td>\n",
       "      <td>...</td>\n",
       "      <td>47.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1009.4</td>\n",
       "      <td>1009.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2008-12-20</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.8</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SSE</td>\n",
       "      <td>26.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1019.2</td>\n",
       "      <td>1017.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.8</td>\n",
       "      <td>23.2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0   2008-12-01   Albury     13.4     22.9       0.6          NaN       NaN   \n",
       "1   2008-12-02   Albury      7.4     25.1       0.0          NaN       NaN   \n",
       "2   2008-12-03   Albury     12.9     25.7       0.0          NaN       NaN   \n",
       "3   2008-12-04   Albury      9.2     28.0       0.0          NaN       NaN   \n",
       "4   2008-12-05   Albury     17.5     32.3       1.0          NaN       NaN   \n",
       "5   2008-12-06   Albury     14.6     29.7       0.2          NaN       NaN   \n",
       "6   2008-12-07   Albury     14.3     25.0       0.0          NaN       NaN   \n",
       "7   2008-12-08   Albury      7.7     26.7       0.0          NaN       NaN   \n",
       "8   2008-12-09   Albury      9.7     31.9       0.0          NaN       NaN   \n",
       "9   2008-12-10   Albury     13.1     30.1       1.4          NaN       NaN   \n",
       "10  2008-12-11   Albury     13.4     30.4       0.0          NaN       NaN   \n",
       "11  2008-12-12   Albury     15.9     21.7       2.2          NaN       NaN   \n",
       "12  2008-12-13   Albury     15.9     18.6      15.6          NaN       NaN   \n",
       "13  2008-12-14   Albury     12.6     21.0       3.6          NaN       NaN   \n",
       "14  2008-12-15   Albury      8.4     24.6       0.0          NaN       NaN   \n",
       "15  2008-12-16   Albury      9.8     27.7       NaN          NaN       NaN   \n",
       "16  2008-12-17   Albury     14.1     20.9       0.0          NaN       NaN   \n",
       "17  2008-12-18   Albury     13.5     22.9      16.8          NaN       NaN   \n",
       "18  2008-12-19   Albury     11.2     22.5      10.6          NaN       NaN   \n",
       "19  2008-12-20   Albury      9.8     25.6       0.0          NaN       NaN   \n",
       "\n",
       "   WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  Humidity3pm  \\\n",
       "0            W           44.0          W  ...        71.0         22.0   \n",
       "1          WNW           44.0        NNW  ...        44.0         25.0   \n",
       "2          WSW           46.0          W  ...        38.0         30.0   \n",
       "3           NE           24.0         SE  ...        45.0         16.0   \n",
       "4            W           41.0        ENE  ...        82.0         33.0   \n",
       "5          WNW           56.0          W  ...        55.0         23.0   \n",
       "6            W           50.0         SW  ...        49.0         19.0   \n",
       "7            W           35.0        SSE  ...        48.0         19.0   \n",
       "8          NNW           80.0         SE  ...        42.0          9.0   \n",
       "9            W           28.0          S  ...        58.0         27.0   \n",
       "10           N           30.0        SSE  ...        48.0         22.0   \n",
       "11         NNE           31.0         NE  ...        89.0         91.0   \n",
       "12           W           61.0        NNW  ...        76.0         93.0   \n",
       "13          SW           44.0          W  ...        65.0         43.0   \n",
       "14         NaN            NaN          S  ...        57.0         32.0   \n",
       "15         WNW           50.0        NaN  ...        50.0         28.0   \n",
       "16         ENE           22.0        SSW  ...        69.0         82.0   \n",
       "17           W           63.0          N  ...        80.0         65.0   \n",
       "18         SSE           43.0        WSW  ...        47.0         32.0   \n",
       "19         SSE           26.0         SE  ...        45.0         26.0   \n",
       "\n",
       "    Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  \\\n",
       "0        1007.7       1007.1       8.0       NaN     16.9     21.8         No   \n",
       "1        1010.6       1007.8       NaN       NaN     17.2     24.3         No   \n",
       "2        1007.6       1008.7       NaN       2.0     21.0     23.2         No   \n",
       "3        1017.6       1012.8       NaN       NaN     18.1     26.5         No   \n",
       "4        1010.8       1006.0       7.0       8.0     17.8     29.7         No   \n",
       "5        1009.2       1005.4       NaN       NaN     20.6     28.9         No   \n",
       "6        1009.6       1008.2       1.0       NaN     18.1     24.6         No   \n",
       "7        1013.4       1010.1       NaN       NaN     16.3     25.5         No   \n",
       "8        1008.9       1003.6       NaN       NaN     18.3     30.2         No   \n",
       "9        1007.0       1005.7       NaN       NaN     20.1     28.2        Yes   \n",
       "10       1011.8       1008.7       NaN       NaN     20.4     28.8         No   \n",
       "11       1010.5       1004.2       8.0       8.0     15.9     17.0        Yes   \n",
       "12        994.3        993.0       8.0       8.0     17.4     15.8        Yes   \n",
       "13       1001.2       1001.8       NaN       7.0     15.8     19.8        Yes   \n",
       "14       1009.7       1008.7       NaN       NaN     15.9     23.5         No   \n",
       "15       1013.4       1010.3       0.0       NaN     17.3     26.2        NaN   \n",
       "16       1012.2       1010.4       8.0       1.0     17.2     18.1         No   \n",
       "17       1005.8       1002.2       8.0       1.0     18.0     21.5        Yes   \n",
       "18       1009.4       1009.7       NaN       2.0     15.5     21.0        Yes   \n",
       "19       1019.2       1017.1       NaN       NaN     15.8     23.2         No   \n",
       "\n",
       "    RainTomorrow  \n",
       "0             No  \n",
       "1             No  \n",
       "2             No  \n",
       "3             No  \n",
       "4             No  \n",
       "5             No  \n",
       "6             No  \n",
       "7             No  \n",
       "8            Yes  \n",
       "9             No  \n",
       "10           Yes  \n",
       "11           Yes  \n",
       "12           Yes  \n",
       "13            No  \n",
       "14           NaN  \n",
       "15            No  \n",
       "16           Yes  \n",
       "17           Yes  \n",
       "18            No  \n",
       "19            No  \n",
       "\n",
       "[20 rows x 23 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'weatherAUS.csv')\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8e517a4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Albury', 'BadgerysCreek', 'Cobar', 'CoffsHarbour', 'Moree',\n",
       "       'Newcastle', 'NorahHead', 'NorfolkIsland', 'Penrith', 'Richmond',\n",
       "       'Sydney', 'SydneyAirport', 'WaggaWagga', 'Williamtown',\n",
       "       'Wollongong', 'Canberra', 'Tuggeranong', 'MountGinini', 'Ballarat',\n",
       "       'Bendigo', 'Sale', 'MelbourneAirport', 'Melbourne', 'Mildura',\n",
       "       'Nhil', 'Portland', 'Watsonia', 'Dartmoor', 'Brisbane', 'Cairns',\n",
       "       'GoldCoast', 'Townsville', 'Adelaide', 'MountGambier', 'Nuriootpa',\n",
       "       'Woomera', 'Albany', 'Witchcliffe', 'PearceRAAF', 'PerthAirport',\n",
       "       'Perth', 'SalmonGums', 'Walpole', 'Hobart', 'Launceston',\n",
       "       'AliceSprings', 'Darwin', 'Katherine', 'Uluru'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Location.unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fe02218b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.9, 25.1, 25.7, 28. , 32.3, 29.7, 25. , 26.7, 31.9, 30.1, 30.4,\n",
       "       21.7, 18.6, 21. , 24.6, 27.7, 20.9, 22.5, 25.6, 29.3, 33. , 31.8,\n",
       "       30.9, 32.4, 33.9, 32.7, 27.2, 24.2, 24.4, 26.5, 23.9, 28.8, 34.6,\n",
       "       35.8, 37.9, 38.9, 28.3, 28.4, 30.8, 32. , 34.7, 37.7, 43. , 32.2,\n",
       "       36.6, 39.9, 38.1, 34. , 35.2, 30.6, 34.3, 38.4, 38.2, 40.7, 41.5,\n",
       "       42.9, 42.7, 43.1, 38.3, 36.9, 41.2, 42.2, 44.8, 40.2, 31.2, 27.3,\n",
       "       21.6, 29. , 29.2, 31.3, 31.1, 29.1, 31.7, 33.2, 29.6, 32.6, 34.5,\n",
       "       30.3, 22.1, 22. , 24. , 27.9, 30.2, 30.5, 25.8, 27. , 19.7, 21.9,\n",
       "       25.3, 33.5, 33.6, 30. , 31.6, 22.3, 29.8, 27.6, 28.9, 29.9, 32.1,\n",
       "       28.1, 21.4, 24.9, 25.4, 20. , 23.4, 22.7, 16.2, 15.8, 12.9, 11.5,\n",
       "       14.5, 12.2, 16.5, 17. , 19.2, 18.9, 19.1, 18.8, 19.3, 18.4, 19. ,\n",
       "       20.5, 19.5, 17.7, 18.5, 15.1, 16.3, 16.6, 16.4, 15.6, 19.8, 21.1,\n",
       "       20.3, 18.1, 15.7, 16.8, 17.1, 14.3, 13.4, 17.4, 16.1, 10.5, 11.6,\n",
       "       12. ,  8.8,  9.6,  8.2, 12.4, 14.9, 15.9, 14.7, 15.3, 17.3, 15.5,\n",
       "       14.1, 13.7, 11.9, 12.3, 13.3, 13.2, 12.1, 12.5, 13.8, 15. , 13.5,\n",
       "       13.1, 11. , 12.6, 14.4,  9.8, 12.7, 13.9, 14.8, 15.2, 17.5, 13.6,\n",
       "       12.8, 16.7, 17.9, 11.1, 14.2, 20.7, 16.9, 17.6,  nan, 24.7, 17.8,\n",
       "       17.2, 20.4, 20.6, 20.2, 18.2, 18.7, 23.3, 26.6, 27.1, 23.1, 24.1,\n",
       "       26.8, 26.9, 24.3, 23.2, 33.4, 36. , 35.1, 32.8, 35.4, 36.3, 39.7,\n",
       "       23.6, 32.9, 26.2, 26.4, 31.5, 27.5, 30.7, 27.4, 22.6, 38.6, 40.3,\n",
       "       33.7, 35.9, 36.8, 33.8, 36.4, 39.8, 42.4, 25.2, 37.3, 41.8, 34.4,\n",
       "       36.5, 35.6, 26.1, 35. , 28.5, 31. , 26.3, 25.5, 22.4, 28.6, 31.4,\n",
       "       28.7, 24.8, 19.9, 23.5, 25.9, 23. , 10.2, 14. ,  8.4,  8.3,  9.4,\n",
       "       10.9, 10.3, 10.8, 14.6, 15.4, 10. , 10.1, 11.8, 16. , 10.4, 13. ,\n",
       "       11.7, 10.7, 19.6, 20.1, 20.8, 23.7, 18. , 21.8, 22.2, 24.5, 29.4,\n",
       "       21.2, 32.5, 28.2, 21.5, 22.8, 11.2, 19.4, 18.3,  9.2,  9.3, 21.3,\n",
       "       23.8, 26. , 27.8, 33.1, 34.9, 36.1, 36.2, 35.3, 35.5,  9.7, 10.6,\n",
       "       11.3, 11.4, 36.7, 37.4, 43.4, 42. , 40.4, 39.2, 38.8, 37. , 35.7,\n",
       "       37.1, 29.5, 37.6, 40.5, 39. , 43.6, 42.6, 34.2, 33.3, 40.6, 41.7,\n",
       "       41.6, 41.1, 40.1,  8.7, 39.6, 34.1,  9.9,  8.6,  9.1,  6.8,  9.5,\n",
       "        9. , 37.2, 34.8, 40. , 38. , 39.5, 40.9, 38.5,  7.5, 39.3, 42.5,\n",
       "       43.7, 37.8, 38.7, 41.9, 39.4, 41.4, 37.5, 45.8, 41. , 39.1, 40.8,\n",
       "       42.8, 45.1, 44.4, 43.5, 42.1, 46.4, 43.9, 45.4, 41.3, 44.3, 46. ,\n",
       "       43.8, 43.2, 45.2,  8.5, 42.3,  7.8, 45.7, 46.6, 47.3, 44.5, 44.1,\n",
       "       44. , 43.3, 46.5, 44.9, 46.9, 45.3, 44.6, 47. ,  8.9,  8.1,  7.6,\n",
       "        6.3,  8. ,  6.6, 45. , 45.5,  4.1,  7.7,  7.1,  7.9,  7. ,  7.3,\n",
       "        2.6,  7.4,  6.2,  7.2,  6. ,  4.9, -1.7,  0. ,  2.4,  5.3,  2.3,\n",
       "        5.7,  5.2,  5.5,  4.7,  6.5,  2.5,  2.7,  3.4,  2.9,  5. ,  3.2,\n",
       "        0.1, -0.1, -2.2, -0.6,  0.6,  2.2,  4. ,  3.5,  3. ,  4.2,  3.3,\n",
       "        3.9,  0.4,  3.8,  5.9, -0.7, -1.8, -0.2,  0.9, -1.3,  1.8,  3.1,\n",
       "        1.6,  0.3,  1.2, -0.5,  1. ,  0.5,  0.2,  1.9,  2. ,  5.1,  2.8,\n",
       "        6.7,  4.3, -2.4, -0.3,  4.6,  6.9, -2.5,  2.1,  1.4,  5.6,  3.6,\n",
       "        4.4,  5.4, -2.7, -1. ,  5.8,  4.8,  1.1,  1.3, -1.4, -0.8, -2. ,\n",
       "        4.5, -0.4,  1.7, -0.9, -2.1, -1.5, -3.1,  6.1, -1.2,  6.4,  3.7,\n",
       "       -1.1,  1.5, -1.9,  0.8, -2.3, -3.7, -2.9, -3.8,  0.7, -4.1, -3.2,\n",
       "       -3. , -4.8, 44.2, 46.8, 46.7, 46.3, 44.7, 45.6, 46.2, 48.1, 46.1])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.MaxTemp.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b4162510",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date              object\n",
       "Location          object\n",
       "MinTemp          float64\n",
       "MaxTemp          float64\n",
       "Rainfall         float64\n",
       "Evaporation      float64\n",
       "Sunshine         float64\n",
       "WindGustDir       object\n",
       "WindGustSpeed    float64\n",
       "WindDir9am        object\n",
       "WindDir3pm        object\n",
       "WindSpeed9am     float64\n",
       "WindSpeed3pm     float64\n",
       "Humidity9am      float64\n",
       "Humidity3pm      float64\n",
       "Pressure9am      float64\n",
       "Pressure3pm      float64\n",
       "Cloud9am         float64\n",
       "Cloud3pm         float64\n",
       "Temp9am          float64\n",
       "Temp3pm          float64\n",
       "RainToday         object\n",
       "RainTomorrow      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Building wheels for collected packages: plotly-resampler, pymeeus\n",
      "  Building wheel for plotly-resampler (pyproject.toml): started\n",
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      "  Created wheel for plotly-resampler: filename=plotly_resampler-0.8.3.2-cp39-cp39-win_amd64.whl size=47906 sha256=104b57c726fd0dfee4a7e7bd846c31317baf27bc6cad3eab232e336130bc770e\n",
      "  Stored in directory: c:\\users\\checkout\\appdata\\local\\pip\\cache\\wheels\\9e\\36\\06\\4c11e300918011376af149098621ec7ebe06d8256566d43d51\n",
      "  Building wheel for pymeeus (setup.py): started\n",
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      "  Created wheel for pymeeus: filename=PyMeeus-0.5.12-py3-none-any.whl size=732001 sha256=8f3df44692fda656d3b96bb2c5fc594909cf30caa02621e424f0ab0e987602b8\n",
      "  Stored in directory: c:\\users\\checkout\\appdata\\local\\pip\\cache\\wheels\\04\\1f\\e5\\8dd0c661cd8d252817655dc14a84f7ae045d6616594145aa81\n",
      "Successfully built plotly-resampler pymeeus\n",
      "Installing collected packages: trace-updater, pymeeus, korean-lunar-calendar, ephem, dash-table, dash-html-components, dash-core-components, widgetsnbextension, tzdata, torch, retrying, orjson, multidict, jupyterlab-widgets, hijri-converter, frozenlist, convertdate, async-timeout, ansi2html, yarl, torchmetrics, plotly, LunarCalendar, lightning-utilities, holidays, aiosignal, dash, captum, aiohttp, jupyter-dash, ipywidgets, pytorch-lightning, plotly-resampler, neuralprophet\n",
      "  Attempting uninstall: widgetsnbextension\n",
      "    Found existing installation: widgetsnbextension 3.5.2\n",
      "    Uninstalling widgetsnbextension-3.5.2:\n",
      "      Successfully uninstalled widgetsnbextension-3.5.2\n",
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      "    Found existing installation: jupyterlab-widgets 1.0.0\n",
      "    Uninstalling jupyterlab-widgets-1.0.0:\n",
      "      Successfully uninstalled jupyterlab-widgets-1.0.0\n",
      "  Attempting uninstall: plotly\n",
      "    Found existing installation: plotly 5.9.0\n",
      "    Uninstalling plotly-5.9.0:\n",
      "      Successfully uninstalled plotly-5.9.0\n",
      "  Attempting uninstall: ipywidgets\n",
      "    Found existing installation: ipywidgets 7.6.5\n",
      "    Uninstalling ipywidgets-7.6.5:\n",
      "      Successfully uninstalled ipywidgets-7.6.5\n",
      "Successfully installed LunarCalendar-0.0.9 aiohttp-3.8.4 aiosignal-1.3.1 ansi2html-1.8.0 async-timeout-4.0.2 captum-0.6.0 convertdate-2.4.0 dash-2.8.1 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 ephem-4.1.4 frozenlist-1.3.3 hijri-converter-2.2.4 holidays-0.22 ipywidgets-8.0.6 jupyter-dash-0.4.2 jupyterlab-widgets-3.0.7 korean-lunar-calendar-0.3.1 lightning-utilities-0.8.0 multidict-6.0.4 neuralprophet-0.5.3 orjson-3.8.9 plotly-5.14.1 plotly-resampler-0.8.3.2 pymeeus-0.5.12 pytorch-lightning-1.9.4 retrying-1.3.4 torch-1.13.1 torchmetrics-0.11.4 trace-updater-0.0.9 tzdata-2023.3 widgetsnbextension-4.0.7 yarl-1.8.2\n"
     ]
    }
   ],
   "source": [
    "df.dtypes  #Date is an object so convert to datetime object\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6b10f667",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''We only want to work with Melbourne's data'''\n",
    "df_melb = df[df['Location']=='Melbourne']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5768eefb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2713.000000</td>\n",
       "      <td>2712.000000</td>\n",
       "      <td>2435.000000</td>\n",
       "      <td>3190.000000</td>\n",
       "      <td>3192.000000</td>\n",
       "      <td>3179.000000</td>\n",
       "      <td>3191.00000</td>\n",
       "      <td>3193.000000</td>\n",
       "      <td>2711.000000</td>\n",
       "      <td>2706.000000</td>\n",
       "      <td>2713.000000</td>\n",
       "      <td>2710.000000</td>\n",
       "      <td>2159.000000</td>\n",
       "      <td>2087.000000</td>\n",
       "      <td>2712.000000</td>\n",
       "      <td>2709.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.779174</td>\n",
       "      <td>20.769358</td>\n",
       "      <td>1.870062</td>\n",
       "      <td>4.650282</td>\n",
       "      <td>6.385307</td>\n",
       "      <td>45.610255</td>\n",
       "      <td>19.12880</td>\n",
       "      <td>22.097401</td>\n",
       "      <td>67.545555</td>\n",
       "      <td>51.180710</td>\n",
       "      <td>1017.634427</td>\n",
       "      <td>1015.778450</td>\n",
       "      <td>5.314034</td>\n",
       "      <td>5.336368</td>\n",
       "      <td>14.596091</td>\n",
       "      <td>19.264489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.208313</td>\n",
       "      <td>6.036381</td>\n",
       "      <td>5.137060</td>\n",
       "      <td>3.320880</td>\n",
       "      <td>3.904811</td>\n",
       "      <td>15.982547</td>\n",
       "      <td>11.58588</td>\n",
       "      <td>9.981196</td>\n",
       "      <td>14.689456</td>\n",
       "      <td>16.131308</td>\n",
       "      <td>7.806738</td>\n",
       "      <td>7.621166</td>\n",
       "      <td>2.459786</td>\n",
       "      <td>2.280631</td>\n",
       "      <td>4.786288</td>\n",
       "      <td>5.748057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.400000</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>988.900000</td>\n",
       "      <td>988.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>7.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.700000</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>11.00000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1012.600000</td>\n",
       "      <td>1010.700000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.275000</td>\n",
       "      <td>14.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.400000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>1017.900000</td>\n",
       "      <td>1016.150000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>14.100000</td>\n",
       "      <td>18.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14.600000</td>\n",
       "      <td>24.200000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>26.00000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>1023.000000</td>\n",
       "      <td>1021.100000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>22.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.600000</td>\n",
       "      <td>46.400000</td>\n",
       "      <td>82.200000</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>13.900000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>67.00000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1039.000000</td>\n",
       "      <td>1035.800000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>45.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           MinTemp      MaxTemp     Rainfall  Evaporation     Sunshine  \\\n",
       "count  2713.000000  2712.000000  2435.000000  3190.000000  3192.000000   \n",
       "mean     11.779174    20.769358     1.870062     4.650282     6.385307   \n",
       "std       4.208313     6.036381     5.137060     3.320880     3.904811   \n",
       "min       1.400000     9.700000     0.000000     0.000000     0.000000   \n",
       "25%       8.700000    16.100000     0.000000     2.200000     3.100000   \n",
       "50%      11.400000    19.500000     0.000000     4.000000     6.500000   \n",
       "75%      14.600000    24.200000     1.200000     6.400000     9.600000   \n",
       "max      28.600000    46.400000    82.200000    23.800000    13.900000   \n",
       "\n",
       "       WindGustSpeed  WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  \\\n",
       "count    3179.000000    3191.00000   3193.000000  2711.000000  2706.000000   \n",
       "mean       45.610255      19.12880     22.097401    67.545555    51.180710   \n",
       "std        15.982547      11.58588      9.981196    14.689456    16.131308   \n",
       "min        11.000000       0.00000      0.000000    14.000000     6.000000   \n",
       "25%        33.000000      11.00000     15.000000    58.000000    41.000000   \n",
       "50%        43.000000      17.00000     20.000000    68.000000    51.000000   \n",
       "75%        56.000000      26.00000     28.000000    78.000000    61.000000   \n",
       "max       122.000000      67.00000     76.000000   100.000000   100.000000   \n",
       "\n",
       "       Pressure9am  Pressure3pm     Cloud9am     Cloud3pm      Temp9am  \\\n",
       "count  2713.000000  2710.000000  2159.000000  2087.000000  2712.000000   \n",
       "mean   1017.634427  1015.778450     5.314034     5.336368    14.596091   \n",
       "std       7.806738     7.621166     2.459786     2.280631     4.786288   \n",
       "min     988.900000   988.300000     0.000000     0.000000     2.900000   \n",
       "25%    1012.600000  1010.700000     3.000000     4.000000    11.275000   \n",
       "50%    1017.900000  1016.150000     7.000000     6.000000    14.100000   \n",
       "75%    1023.000000  1021.100000     7.000000     7.000000    17.400000   \n",
       "max    1039.000000  1035.800000     8.000000     8.000000    35.500000   \n",
       "\n",
       "           Temp3pm  \n",
       "count  2709.000000  \n",
       "mean     19.264489  \n",
       "std       5.748057  \n",
       "min       7.200000  \n",
       "25%      14.900000  \n",
       "50%      18.200000  \n",
       "75%      22.500000  \n",
       "max      45.400000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_melb.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "78db9816",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Checkout\\AppData\\Local\\Temp\\ipykernel_17288\\1094022065.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_melb['Date'] = pd.to_datetime(df_melb['Date'])\n"
     ]
    }
   ],
   "source": [
    "'''Always Need to convert date column from object to datetime format for any time series model'''\n",
    "df_melb['Date'] = pd.to_datetime(df_melb['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fcdd4343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date             datetime64[ns]\n",
       "Location                 object\n",
       "MinTemp                 float64\n",
       "MaxTemp                 float64\n",
       "Rainfall                float64\n",
       "Evaporation             float64\n",
       "Sunshine                float64\n",
       "WindGustDir              object\n",
       "WindGustSpeed           float64\n",
       "WindDir9am               object\n",
       "WindDir3pm               object\n",
       "WindSpeed9am            float64\n",
       "WindSpeed3pm            float64\n",
       "Humidity9am             float64\n",
       "Humidity3pm             float64\n",
       "Pressure9am             float64\n",
       "Pressure3pm             float64\n",
       "Cloud9am                float64\n",
       "Cloud3pm                float64\n",
       "Temp9am                 float64\n",
       "Temp3pm                 float64\n",
       "RainToday                object\n",
       "RainTomorrow             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_melb.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d87d29e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145460"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bef95710",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3193"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_melb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "03b13f7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_melb['Date'], df_melb['Temp3pm'])\n",
    "plt.show()\n",
    "# Missing data from 2015 - mid 2016 and we want to make sure we dont pass missing values to NeuralProphet so we will cutoff our dataset from 2015\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "59933d27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering dataframe to have values till 2015 \n",
    "df_melb_2015 = df_melb[df_melb['Date'].dt.year<=2015]\n",
    "df_melb_2015['Date'].dt.year.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bf372891",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_melb_2015.plot('Date','Temp3pm')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edaaa5ef",
   "metadata": {},
   "source": [
    "# Train the Model\n",
    "NeuralProphet expects only the date column and the column we want to predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "49bdd4e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Temp3pm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67200</th>\n",
       "      <td>2008-07-01</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67201</th>\n",
       "      <td>2008-07-02</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67202</th>\n",
       "      <td>2008-07-03</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67203</th>\n",
       "      <td>2008-07-04</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67204</th>\n",
       "      <td>2008-07-05</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Temp3pm\n",
       "67200 2008-07-01     14.6\n",
       "67201 2008-07-02     13.7\n",
       "67202 2008-07-03     13.9\n",
       "67203 2008-07-04     13.1\n",
       "67204 2008-07-05     14.6"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df_melb_2015[['Date','Temp3pm']]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b715d59f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         0\n",
       "Temp3pm    363\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum() #checking for missing values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "02151c91",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Checkout\\AppData\\Local\\Temp\\ipykernel_17288\\3002767713.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data.dropna(how = 'any', inplace=True) # When how='any', the method drops any row that contains at least one missing value, and when how='all', the method drops only rows where all columns contain missing values.\n"
     ]
    }
   ],
   "source": [
    "data.dropna(how = 'any', inplace=True) # When how='any', the method drops any row that contains at least one missing value, and when how='all', the method drops only rows where all columns contain missing values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0f317c4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date       0\n",
       "Temp3pm    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bc933fc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Temp3pm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-07-01</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-07-02</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-07-03</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-07-04</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-07-05</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Temp3pm\n",
       "0 2008-07-01     14.6\n",
       "1 2008-07-02     13.7\n",
       "2 2008-07-03     13.9\n",
       "3 2008-07-04     13.1\n",
       "4 2008-07-05     14.6"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.reset_index(drop=True,inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "136cdca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2288"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "50e72fc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = data.iloc[:2000,:]\n",
    "data_test = data.iloc[2000:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f11e9b51",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle  #to save our trained model as an object that can be resued by other coders\n",
    "from neuralprophet import NeuralProphet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c0817ff3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.columns = ['ds', 'y'] #required naming convention for model to understand input and output columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "63ee4d35",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING - (NP.forecaster.fit) - When Global modeling with local normalization, metrics are displayed in normalized scale.\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.65% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n",
      "INFO - (NP.config.init_data_params) - Setting normalization to global as only one dataframe provided for training.\n",
      "INFO - (NP.utils.set_auto_seasonalities) - Disabling daily seasonality. Run NeuralProphet with daily_seasonality=True to override this.\n",
      "INFO - (NP.config.set_auto_batch_epoch) - Auto-set batch_size to 32\n",
      "INFO - (NP.config.set_auto_batch_epoch) - Auto-set epochs to 158\n",
      "WARNING - (NP.config.set_lr_finder_args) - Learning rate finder: The number of batches (63) is too small than the required number for the learning rate finder (233). The results might not be optimal.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b12a6b65d77467a90e2b36acc9978ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Finding best initial lr:   0%|          | 0/233 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "729555c13b914a78873c459dc6558062",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = NeuralProphet()\n",
    "model = m.fit(data_train, freq='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c58db926",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test.columns = ['ds','y']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4efc5698",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.653% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.653% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f287d6e6f7d47229c852a05d5743166",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 63it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>yhat1</th>\n",
       "      <th>trend</th>\n",
       "      <th>season_yearly</th>\n",
       "      <th>season_weekly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>18.4</td>\n",
       "      <td>23.181061</td>\n",
       "      <td>19.838364</td>\n",
       "      <td>3.682957</td>\n",
       "      <td>-0.340260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-03-25</td>\n",
       "      <td>24.4</td>\n",
       "      <td>23.758156</td>\n",
       "      <td>19.838612</td>\n",
       "      <td>3.591372</td>\n",
       "      <td>0.328170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-03-26</td>\n",
       "      <td>26.3</td>\n",
       "      <td>23.358063</td>\n",
       "      <td>19.838860</td>\n",
       "      <td>3.494935</td>\n",
       "      <td>0.024268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-03-27</td>\n",
       "      <td>25.3</td>\n",
       "      <td>23.392818</td>\n",
       "      <td>19.839108</td>\n",
       "      <td>3.393697</td>\n",
       "      <td>0.160011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-03-28</td>\n",
       "      <td>21.5</td>\n",
       "      <td>23.167885</td>\n",
       "      <td>19.839355</td>\n",
       "      <td>3.287749</td>\n",
       "      <td>0.040781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>23.7</td>\n",
       "      <td>24.639404</td>\n",
       "      <td>19.908512</td>\n",
       "      <td>4.570880</td>\n",
       "      <td>0.160011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>37.7</td>\n",
       "      <td>24.659428</td>\n",
       "      <td>19.908758</td>\n",
       "      <td>4.709889</td>\n",
       "      <td>0.040781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>37.4</td>\n",
       "      <td>24.626785</td>\n",
       "      <td>19.909006</td>\n",
       "      <td>4.849117</td>\n",
       "      <td>-0.131340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>21.8</td>\n",
       "      <td>24.815479</td>\n",
       "      <td>19.909254</td>\n",
       "      <td>4.987853</td>\n",
       "      <td>-0.081630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>22.4</td>\n",
       "      <td>24.694614</td>\n",
       "      <td>19.909502</td>\n",
       "      <td>5.125373</td>\n",
       "      <td>-0.340260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds     y      yhat1      trend  season_yearly  season_weekly\n",
       "0   2014-03-24  18.4  23.181061  19.838364       3.682957      -0.340260\n",
       "1   2014-03-25  24.4  23.758156  19.838612       3.591372       0.328170\n",
       "2   2014-03-26  26.3  23.358063  19.838860       3.494935       0.024268\n",
       "3   2014-03-27  25.3  23.392818  19.839108       3.393697       0.160011\n",
       "4   2014-03-28  21.5  23.167885  19.839355       3.287749       0.040781\n",
       "..         ...   ...        ...        ...            ...            ...\n",
       "283 2015-01-01  23.7  24.639404  19.908512       4.570880       0.160011\n",
       "284 2015-01-02  37.7  24.659428  19.908758       4.709889       0.040781\n",
       "285 2015-01-03  37.4  24.626785  19.909006       4.849117      -0.131340\n",
       "286 2015-01-04  21.8  24.815479  19.909254       4.987853      -0.081630\n",
       "287 2015-01-05  22.4  24.694614  19.909502       5.125373      -0.340260\n",
       "\n",
       "[288 rows x 6 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = m.predict(data_test)\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4c953ea4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Requirement already satisfied: MarkupSafe>=0.23 in c:\\users\\checkout\\anaconda3\\lib\\site-packages (from Jinja2>=2.10.1->Flask>=1.0.4->dash<2.9->neuralprophet) (2.0.1)\n",
      "Requirement already satisfied: entrypoints in c:\\users\\checkout\\anaconda3\\lib\\site-packages (from jupyter-client>=6.1.12->ipykernel>=4.5.1->ipywidgets>=7.5.1->neuralprophet) (0.4)\n",
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      "Requirement already satisfied: attrs>=17.3.0 in c:\\users\\checkout\\anaconda3\\lib\\site-packages (from aiohttp->fsspec[http]>2021.06.0->pytorch-lightning<2.0.0,>=1.7->neuralprophet) (21.4.0)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in c:\\users\\checkout\\anaconda3\\lib\\site-packages (from aiohttp->fsspec[http]>2021.06.0->pytorch-lightning<2.0.0,>=1.7->neuralprophet) (1.3.3)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in c:\\users\\checkout\\anaconda3\\lib\\site-packages (from aiohttp->fsspec[http]>2021.06.0->pytorch-lightning<2.0.0,>=1.7->neuralprophet) (1.3.1)\n",
      "Requirement already satisfied: pywin32>=1.0 in c:\\users\\checkout\\anaconda3\\lib\\site-packages (from jupyter-core>=4.9.2->jupyter-client>=6.1.12->ipykernel>=4.5.1->ipywidgets>=7.5.1->neuralprophet) (302)\n"
     ]
    }
   ],
   "source": [
    "!pip install neuralprophet\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8252391d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      -4.781061\n",
      "1       0.641844\n",
      "2       2.941937\n",
      "3       1.907182\n",
      "4      -1.667885\n",
      "         ...    \n",
      "283    -0.939404\n",
      "284    13.040572\n",
      "285    12.773215\n",
      "286    -3.015479\n",
      "287    -2.294614\n",
      "Name: Score_diff, Length: 288, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "output['Residuals'] = output['y'] - output['yhat1']\n",
    "print (output['Score_diff'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "069785ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.6680959827370128"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(output['Residuals'].apply(lambda x:abs(x)))  #test error\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "778d35b1",
   "metadata": {},
   "source": [
    "# Future forecast and visualizing trends\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "07471fe0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.65% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n",
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.889% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.889% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "533012123f354320944ef4d4b9d6ef69",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 63it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>yhat1</th>\n",
       "      <th>trend</th>\n",
       "      <th>season_yearly</th>\n",
       "      <th>season_weekly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>None</td>\n",
       "      <td>23.181061</td>\n",
       "      <td>19.838364</td>\n",
       "      <td>3.682957</td>\n",
       "      <td>-0.340260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-03-25</td>\n",
       "      <td>None</td>\n",
       "      <td>23.758156</td>\n",
       "      <td>19.838612</td>\n",
       "      <td>3.591372</td>\n",
       "      <td>0.328170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-03-26</td>\n",
       "      <td>None</td>\n",
       "      <td>23.358063</td>\n",
       "      <td>19.838860</td>\n",
       "      <td>3.494935</td>\n",
       "      <td>0.024268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-03-27</td>\n",
       "      <td>None</td>\n",
       "      <td>23.392818</td>\n",
       "      <td>19.839108</td>\n",
       "      <td>3.393697</td>\n",
       "      <td>0.160011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-03-28</td>\n",
       "      <td>None</td>\n",
       "      <td>23.167885</td>\n",
       "      <td>19.839355</td>\n",
       "      <td>3.287749</td>\n",
       "      <td>0.040781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>2016-09-04</td>\n",
       "      <td>None</td>\n",
       "      <td>17.034435</td>\n",
       "      <td>20.060204</td>\n",
       "      <td>-2.944140</td>\n",
       "      <td>-0.081630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>2016-09-05</td>\n",
       "      <td>None</td>\n",
       "      <td>16.867041</td>\n",
       "      <td>20.060452</td>\n",
       "      <td>-2.853152</td>\n",
       "      <td>-0.340260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>2016-09-06</td>\n",
       "      <td>None</td>\n",
       "      <td>17.623615</td>\n",
       "      <td>20.060699</td>\n",
       "      <td>-2.765256</td>\n",
       "      <td>0.328170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>2016-09-07</td>\n",
       "      <td>None</td>\n",
       "      <td>17.404476</td>\n",
       "      <td>20.060947</td>\n",
       "      <td>-2.680739</td>\n",
       "      <td>0.024268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>None</td>\n",
       "      <td>17.621353</td>\n",
       "      <td>20.061195</td>\n",
       "      <td>-2.599853</td>\n",
       "      <td>0.160011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>900 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds     y      yhat1      trend  season_yearly  season_weekly\n",
       "0   2014-03-24  None  23.181061  19.838364       3.682957      -0.340260\n",
       "1   2014-03-25  None  23.758156  19.838612       3.591372       0.328170\n",
       "2   2014-03-26  None  23.358063  19.838860       3.494935       0.024268\n",
       "3   2014-03-27  None  23.392818  19.839108       3.393697       0.160011\n",
       "4   2014-03-28  None  23.167885  19.839355       3.287749       0.040781\n",
       "..         ...   ...        ...        ...            ...            ...\n",
       "895 2016-09-04  None  17.034435  20.060204      -2.944140      -0.081630\n",
       "896 2016-09-05  None  16.867041  20.060452      -2.853152      -0.340260\n",
       "897 2016-09-06  None  17.623615  20.060699      -2.765256       0.328170\n",
       "898 2016-09-07  None  17.404476  20.060947      -2.680739       0.024268\n",
       "899 2016-09-08  None  17.621353  20.061195      -2.599853       0.160011\n",
       "\n",
       "[900 rows x 6 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future = m.make_future_dataframe(data_train, periods = 900)\n",
    "forecast = m.predict(future)\n",
    "forecast.head(900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3846fb90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b271cf49d7b545f087659b3d9b9cbe79",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidgetResampler({\n",
       "    'data': [{'fill': 'none',\n",
       "              'line': {'color': 'rgba(45, 146, 255, 1.0)', 'width': 2},\n",
       "              'mode': 'lines',\n",
       "              'name': 'yhat1',\n",
       "              'type': 'scatter',\n",
       "              'uid': '79ee92f4-e284-4169-8cbd-2552ab4659ff',\n",
       "              'x': array([datetime.datetime(2014, 3, 24, 0, 0),\n",
       "                          datetime.datetime(2014, 3, 25, 0, 0),\n",
       "                          datetime.datetime(2014, 3, 26, 0, 0), ...,\n",
       "                          datetime.datetime(2016, 9, 6, 0, 0),\n",
       "                          datetime.datetime(2016, 9, 7, 0, 0),\n",
       "                          datetime.datetime(2016, 9, 8, 0, 0)], dtype=object),\n",
       "              'y': array([23.18106079, 23.75815582, 23.35806274, ..., 17.62361526, 17.40447617,\n",
       "                          17.62135315])},\n",
       "             {'marker': {'color': 'black', 'size': 4},\n",
       "              'mode': 'markers',\n",
       "              'name': 'Actual',\n",
       "              'type': 'scatter',\n",
       "              'uid': '58cef03a-c63d-4925-b79e-33f480a8685f',\n",
       "              'x': array([datetime.datetime(2014, 3, 24, 0, 0),\n",
       "                          datetime.datetime(2014, 3, 25, 0, 0),\n",
       "                          datetime.datetime(2014, 3, 26, 0, 0), ...,\n",
       "                          datetime.datetime(2016, 9, 6, 0, 0),\n",
       "                          datetime.datetime(2016, 9, 7, 0, 0),\n",
       "                          datetime.datetime(2016, 9, 8, 0, 0)], dtype=object),\n",
       "              'y': array([nan, nan, nan, ..., nan, nan, nan])}],\n",
       "    'layout': {'autosize': True,\n",
       "               'font': {'size': 10},\n",
       "               'height': 420,\n",
       "               'hovermode': 'x unified',\n",
       "               'margin': {'b': 0, 'l': 0, 'pad': 0, 'r': 10, 't': 10},\n",
       "               'showlegend': True,\n",
       "               'template': '...',\n",
       "               'title': {'font': {'size': 12}},\n",
       "               'width': 700,\n",
       "               'xaxis': {'linewidth': 1.5,\n",
       "                         'mirror': True,\n",
       "                         'rangeselector': {'buttons': [{'count': 7, 'label': '1w', 'step': 'day', 'stepmode': 'backward'},\n",
       "                                                       {'count': 1,\n",
       "                                                        'label': '1m',\n",
       "                                                        'step': 'month',\n",
       "                                                        'stepmode': 'backward'},\n",
       "                                                       {'count': 6,\n",
       "                                                        'label': '6m',\n",
       "                                                        'step': 'month',\n",
       "                                                        'stepmode': 'backward'},\n",
       "                                                       {'count': 1, 'label': '1y', 'step': 'year', 'stepmode': 'backward'},\n",
       "                                                       {'step': 'all'}]},\n",
       "                         'rangeslider': {'visible': True},\n",
       "                         'showline': True,\n",
       "                         'title': {'text': 'ds'},\n",
       "                         'type': 'date'},\n",
       "               'yaxis': {'linewidth': 1.5, 'mirror': True, 'showline': True, 'title': {'text': 'y'}}}\n",
       "})"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.plot(forecast)   #the predicted graph is similar to what we had seen during EDA which is good\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "89a2e838",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9cbcc1c2bf434257be2c4cc95f3da5a3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidgetResampler({\n",
       "    'data': [{'line': {'color': '#2d92ff', 'width': 2},\n",
       "              'mode': 'lines',\n",
       "              'name': 'Trend',\n",
       "              'showlegend': False,\n",
       "              'type': 'scatter',\n",
       "              'uid': 'a66f9e57-0794-41d5-a9aa-29238684cb14',\n",
       "              'x': array([datetime.datetime(2014, 3, 24, 0, 0),\n",
       "                          datetime.datetime(2014, 3, 25, 0, 0),\n",
       "                          datetime.datetime(2014, 3, 26, 0, 0), ...,\n",
       "                          datetime.datetime(2016, 9, 6, 0, 0),\n",
       "                          datetime.datetime(2016, 9, 7, 0, 0),\n",
       "                          datetime.datetime(2016, 9, 8, 0, 0)], dtype=object),\n",
       "              'xaxis': 'x',\n",
       "              'y': array([19.83836365, 19.8386116 , 19.83885956, ..., 20.06069946, 20.06094742,\n",
       "                          20.06119537]),\n",
       "              'yaxis': 'y'},\n",
       "             {'line': {'color': '#2d92ff', 'width': 2},\n",
       "              'mode': 'lines',\n",
       "              'name': 'yearly seasonality',\n",
       "              'showlegend': False,\n",
       "              'type': 'scatter',\n",
       "              'uid': '514b458e-dd2f-46e1-9af7-4d705f49b2c1',\n",
       "              'x': array([datetime.datetime(2014, 3, 24, 0, 0),\n",
       "                          datetime.datetime(2014, 3, 25, 0, 0),\n",
       "                          datetime.datetime(2014, 3, 26, 0, 0), ...,\n",
       "                          datetime.datetime(2016, 9, 6, 0, 0),\n",
       "                          datetime.datetime(2016, 9, 7, 0, 0),\n",
       "                          datetime.datetime(2016, 9, 8, 0, 0)], dtype=object),\n",
       "              'xaxis': 'x2',\n",
       "              'y': array([ 3.6829567 ,  3.59137249,  3.49493527, ..., -2.76525569, -2.6807394 ,\n",
       "                          -2.59985328]),\n",
       "              'yaxis': 'y2'},\n",
       "             {'line': {'color': '#2d92ff', 'width': 2},\n",
       "              'mode': 'lines',\n",
       "              'name': 'weekly seasonality',\n",
       "              'showlegend': False,\n",
       "              'type': 'scatter',\n",
       "              'uid': 'a42a6adc-19ee-4e65-8dd3-b0f682e6a9fd',\n",
       "              'x': array([datetime.datetime(2014, 3, 24, 0, 0),\n",
       "                          datetime.datetime(2014, 3, 25, 0, 0),\n",
       "                          datetime.datetime(2014, 3, 26, 0, 0), ...,\n",
       "                          datetime.datetime(2016, 9, 6, 0, 0),\n",
       "                          datetime.datetime(2016, 9, 7, 0, 0),\n",
       "                          datetime.datetime(2016, 9, 8, 0, 0)], dtype=object),\n",
       "              'xaxis': 'x3',\n",
       "              'y': array([-0.34025982,  0.32816973,  0.02426784, ...,  0.32816973,  0.02426784,\n",
       "                           0.16001119]),\n",
       "              'yaxis': 'y3'}],\n",
       "    'layout': {'autosize': True,\n",
       "               'font': {'size': 10},\n",
       "               'height': 630,\n",
       "               'hovermode': 'x unified',\n",
       "               'legend': {'traceorder': 'reversed', 'y': 0.1},\n",
       "               'margin': {'b': 0, 'l': 0, 'pad': 0, 'r': 10, 't': 10},\n",
       "               'template': '...',\n",
       "               'title': {'font': {'size': 12}},\n",
       "               'width': 700,\n",
       "               'xaxis': {'anchor': 'y',\n",
       "                         'domain': [0.0, 1.0],\n",
       "                         'linewidth': 1.5,\n",
       "                         'mirror': True,\n",
       "                         'range': [2014-02-07 00:00:00, 2016-10-23 00:00:00],\n",
       "                         'showline': True,\n",
       "                         'title': {'text': 'ds'},\n",
       "                         'type': 'date'},\n",
       "               'xaxis2': {'anchor': 'y2',\n",
       "                          'domain': [0.0, 1.0],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'range': [2014-02-07 00:00:00, 2016-10-23 00:00:00],\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'ds'},\n",
       "                          'type': 'date'},\n",
       "               'xaxis3': {'anchor': 'y3',\n",
       "                          'domain': [0.0, 1.0],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'range': [2014-02-07 00:00:00, 2016-10-23 00:00:00],\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'ds'},\n",
       "                          'type': 'date'},\n",
       "               'yaxis': {'anchor': 'x',\n",
       "                         'domain': [0.7333333333333333, 1.0],\n",
       "                         'linewidth': 1.5,\n",
       "                         'mirror': True,\n",
       "                         'rangemode': 'normal',\n",
       "                         'showline': True,\n",
       "                         'title': {'text': 'Trend'}},\n",
       "               'yaxis2': {'anchor': 'x2',\n",
       "                          'domain': [0.36666666666666664, 0.6333333333333333],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'rangemode': 'tozero',\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'yearly seasonality'}},\n",
       "               'yaxis3': {'anchor': 'x3',\n",
       "                          'domain': [0.0, 0.26666666666666666],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'rangemode': 'tozero',\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'weekly seasonality'}}}\n",
       "})"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.plot_components(forecast)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "169760b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "279c45f827504522a454e10bbd537731",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidgetResampler({\n",
       "    'data': [{'fill': 'none',\n",
       "              'line': {'color': 'rgba(45, 146, 255, 1.0)', 'width': 2},\n",
       "              'mode': 'lines',\n",
       "              'name': 'yhat1',\n",
       "              'type': 'scatter',\n",
       "              'uid': '2cea0fd7-f45b-4f4a-8daf-bc4d45096093',\n",
       "              'x': array([datetime.datetime(2014, 3, 24, 0, 0),\n",
       "                          datetime.datetime(2014, 3, 25, 0, 0),\n",
       "                          datetime.datetime(2014, 3, 26, 0, 0), ...,\n",
       "                          datetime.datetime(2015, 1, 3, 0, 0),\n",
       "                          datetime.datetime(2015, 1, 4, 0, 0),\n",
       "                          datetime.datetime(2015, 1, 5, 0, 0)], dtype=object),\n",
       "              'y': array([23.18106079, 23.75815582, 23.35806274, ..., 24.62678528, 24.81547928,\n",
       "                          24.69461441])},\n",
       "             {'marker': {'color': 'black', 'size': 4},\n",
       "              'mode': 'markers',\n",
       "              'name': 'Actual',\n",
       "              'type': 'scatter',\n",
       "              'uid': '4a3d5ab6-1000-438c-8cca-5bee99735045',\n",
       "              'x': array([datetime.datetime(2014, 3, 24, 0, 0),\n",
       "                          datetime.datetime(2014, 3, 25, 0, 0),\n",
       "                          datetime.datetime(2014, 3, 26, 0, 0), ...,\n",
       "                          datetime.datetime(2015, 1, 3, 0, 0),\n",
       "                          datetime.datetime(2015, 1, 4, 0, 0),\n",
       "                          datetime.datetime(2015, 1, 5, 0, 0)], dtype=object),\n",
       "              'y': array([18.4, 24.4, 26.3, ..., 37.4, 21.8, 22.4])}],\n",
       "    'layout': {'autosize': True,\n",
       "               'font': {'size': 10},\n",
       "               'height': 420,\n",
       "               'hovermode': 'x unified',\n",
       "               'margin': {'b': 0, 'l': 0, 'pad': 0, 'r': 10, 't': 10},\n",
       "               'showlegend': True,\n",
       "               'template': '...',\n",
       "               'title': {'font': {'size': 12}},\n",
       "               'width': 700,\n",
       "               'xaxis': {'linewidth': 1.5,\n",
       "                         'mirror': True,\n",
       "                         'rangeselector': {'buttons': [{'count': 7, 'label': '1w', 'step': 'day', 'stepmode': 'backward'},\n",
       "                                                       {'count': 1,\n",
       "                                                        'label': '1m',\n",
       "                                                        'step': 'month',\n",
       "                                                        'stepmode': 'backward'},\n",
       "                                                       {'count': 6,\n",
       "                                                        'label': '6m',\n",
       "                                                        'step': 'month',\n",
       "                                                        'stepmode': 'backward'},\n",
       "                                                       {'count': 1, 'label': '1y', 'step': 'year', 'stepmode': 'backward'},\n",
       "                                                       {'step': 'all'}]},\n",
       "                         'rangeslider': {'visible': True},\n",
       "                         'showline': True,\n",
       "                         'title': {'text': 'ds'},\n",
       "                         'type': 'date'},\n",
       "               'yaxis': {'linewidth': 1.5, 'mirror': True, 'showline': True, 'title': {'text': 'y'}}}\n",
       "})"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.plot(output)   # how well the model has learnt the trend \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "47b30667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa9bc7cd60f04a75a02ac7ac1e750bb3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidgetResampler({\n",
       "    'data': [{'fill': 'none',\n",
       "              'line': {'color': '#2d92ff', 'width': 2},\n",
       "              'mode': 'lines',\n",
       "              'name': '<b style=\"color:sandybrown\">[R]</b> Trend <i style=\"color:#fc9944\">~2D</i>',\n",
       "              'type': 'scatter',\n",
       "              'uid': '441d9bf2-851b-4604-b318-25de386825f4',\n",
       "              'x': array([datetime.datetime(2008, 7, 1, 0, 0),\n",
       "                          datetime.datetime(2008, 7, 2, 0, 0),\n",
       "                          datetime.datetime(2008, 7, 4, 0, 0), ...,\n",
       "                          datetime.datetime(2014, 3, 19, 0, 0),\n",
       "                          datetime.datetime(2014, 3, 20, 0, 0),\n",
       "                          datetime.datetime(2014, 3, 23, 0, 0)], dtype=object),\n",
       "              'xaxis': 'x',\n",
       "              'y': array([18.79628968, 18.80186416, 18.81301312, ..., 19.83712537, 19.83737324,\n",
       "                          19.83811684]),\n",
       "              'yaxis': 'y'},\n",
       "             {'marker': {'color': '#2d92ff'},\n",
       "              'name': 'Trend Rate Change',\n",
       "              'type': 'bar',\n",
       "              'uid': 'b2e517ab-0e24-4b73-819d-56db5a51b5ea',\n",
       "              'x': array([Timestamp('2008-07-01 00:00:00'),\n",
       "                          Timestamp('2008-11-30 01:44:43.636364'),\n",
       "                          Timestamp('2009-05-01 03:29:27.272727'),\n",
       "                          Timestamp('2009-09-30 05:14:10.909091'),\n",
       "                          Timestamp('2010-03-01 06:58:54.545455'),\n",
       "                          Timestamp('2010-07-31 08:43:38.181818'),\n",
       "                          Timestamp('2010-12-30 10:28:21.818182'),\n",
       "                          Timestamp('2011-05-31 12:13:05.454545'),\n",
       "                          Timestamp('2011-10-30 13:57:49.090909'),\n",
       "                          Timestamp('2012-03-30 15:42:32.727273'),\n",
       "                          Timestamp('2012-08-29 17:27:16.363636'),\n",
       "                          Timestamp('2014-03-23 00:00:00')], dtype=object),\n",
       "              'xaxis': 'x2',\n",
       "              'y': array([ 0.05663849, -0.58819628,  0.52804887, -0.1944579 , -1.32722759,\n",
       "                           0.80118966,  0.59373277,  0.82366163, -2.24546933,  1.59979498,\n",
       "                          -0.46503186,  0.        ]),\n",
       "              'yaxis': 'y2'},\n",
       "             {'fill': 'none',\n",
       "              'line': {'color': '#2d92ff', 'width': 2},\n",
       "              'mode': 'lines',\n",
       "              'name': 'yearly',\n",
       "              'type': 'scatter',\n",
       "              'uid': 'cc02ae59-8b36-4dc4-ad57-2a09e7cfb59c',\n",
       "              'x': array([datetime.datetime(2017, 1, 1, 0, 0),\n",
       "                          datetime.datetime(2017, 1, 2, 0, 0),\n",
       "                          datetime.datetime(2017, 1, 3, 0, 0), ...,\n",
       "                          datetime.datetime(2017, 12, 29, 0, 0),\n",
       "                          datetime.datetime(2017, 12, 30, 0, 0),\n",
       "                          datetime.datetime(2017, 12, 31, 0, 0)], dtype=object),\n",
       "              'xaxis': 'x3',\n",
       "              'y': array([4.64031263, 4.77952039, 4.91859204, ..., 4.19548019, 4.33027307,\n",
       "                          4.46719713]),\n",
       "              'yaxis': 'y3'},\n",
       "             {'fill': 'none',\n",
       "              'line': {'color': '#2d92ff', 'width': 2},\n",
       "              'mode': 'lines',\n",
       "              'name': 'weekly',\n",
       "              'type': 'scatter',\n",
       "              'uid': '65e975c5-a6e7-4412-a582-49049e7997cf',\n",
       "              'x': array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "                           14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "                           28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "                           42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "                           56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "                           70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "                           84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "                           98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "                          112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "                          126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "                          140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "                          154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167]),\n",
       "              'xaxis': 'x4',\n",
       "              'y': array([-8.16297712e-02, -1.00753624e-01, -1.21013708e-01, -1.42173860e-01,\n",
       "                          -1.63981331e-01, -1.86169823e-01, -2.08462731e-01, -2.30576525e-01,\n",
       "                          -2.52224239e-01, -2.73119028e-01, -2.92977743e-01, -3.11524478e-01,\n",
       "                          -3.28494053e-01, -3.43635380e-01, -3.56714674e-01, -3.67518470e-01,\n",
       "                          -3.75856404e-01, -3.81563725e-01, -3.84503508e-01, -3.84568535e-01,\n",
       "                          -3.81682825e-01, -3.75802782e-01, -3.66917968e-01, -3.55051463e-01,\n",
       "                          -3.40259828e-01, -3.22632659e-01, -3.02291740e-01, -2.79389804e-01,\n",
       "                          -2.54108921e-01, -2.26658516e-01, -1.97273068e-01, -1.66209486e-01,\n",
       "                          -1.33744221e-01, -1.00170130e-01, -6.57931386e-02, -3.09287407e-02,\n",
       "                           4.10161985e-03,  3.89742466e-02,  7.33669183e-02,  1.06962601e-01,\n",
       "                           1.39453105e-01,  1.70542631e-01,  1.99951174e-01,  2.27417742e-01,\n",
       "                           2.52703334e-01,  2.75593671e-01,  2.95901615e-01,  3.13469270e-01,\n",
       "                           3.28169728e-01,  3.39908442e-01,  3.48624211e-01,  3.54289761e-01,\n",
       "                           3.56911921e-01,  3.56531386e-01,  3.53222080e-01,  3.47090112e-01,\n",
       "                           3.38272360e-01,  3.26934673e-01,  3.13269743e-01,  2.97494653e-01,\n",
       "                           2.79848138e-01,  2.60587598e-01,  2.39985889e-01,  2.18327947e-01,\n",
       "                           1.95907268e-01,  1.73022309e-01,  1.49972837e-01,  1.27056275e-01,\n",
       "                           1.04564104e-01,  8.27783372e-02,  6.19681433e-02,  4.23866302e-02,\n",
       "                           2.42678485e-02,  7.82404174e-03, -6.75682053e-03, -1.93131974e-02,\n",
       "                          -2.97117933e-02, -3.78490151e-02, -4.36520529e-02, -4.70795720e-02,\n",
       "                          -4.81220086e-02, -4.68014669e-02, -4.31712210e-02, -3.73148276e-02,\n",
       "                          -2.93448628e-02, -1.94013003e-02, -7.64955228e-03,  5.72180204e-03,\n",
       "                           2.05035693e-02,  3.64687442e-02,  5.33755247e-02,  7.09705011e-02,\n",
       "                           8.89919793e-02,  1.07173394e-01,  1.25246769e-01,  1.42946184e-01,\n",
       "                           1.60011197e-01,  1.76190187e-01,  1.91243566e-01,  2.04946827e-01,\n",
       "                           2.17093393e-01,  2.27497213e-01,  2.35995098e-01,  2.42448747e-01,\n",
       "                           2.46746449e-01,  2.48804438e-01,  2.48567882e-01,  2.46011496e-01,\n",
       "                           2.41139777e-01,  2.33986843e-01,  2.24615902e-01,  2.13118339e-01,\n",
       "                           1.99612446e-01,  1.84241797e-01,  1.67173317e-01,  1.48595033e-01,\n",
       "                           1.28713573e-01,  1.07751421e-01,  8.59439698e-02,  6.35364210e-02,\n",
       "                           4.07805542e-02,  1.79314219e-02, -4.75599487e-03, -2.70301233e-02,\n",
       "                          -4.86460872e-02, -6.93689026e-02, -8.89765405e-02, -1.07262817e-01,\n",
       "                          -1.24040074e-01, -1.39141613e-01, -1.52423854e-01, -1.63768191e-01,\n",
       "                          -1.73082521e-01, -1.80302418e-01, -1.85391951e-01, -1.88344127e-01,\n",
       "                          -1.89180947e-01, -1.87953092e-01, -1.84739224e-01, -1.79644919e-01,\n",
       "                          -1.72801251e-01, -1.64363030e-01, -1.54506732e-01, -1.43428141e-01,\n",
       "                          -1.31339729e-01, -1.18467810e-01, -1.05049514e-01, -9.13296031e-02,\n",
       "                          -7.75571821e-02, -6.39823430e-02, -5.08527857e-02, -3.84104592e-02,\n",
       "                          -2.68882658e-02, -1.65068724e-02, -7.47166835e-03,  3.00884419e-05,\n",
       "                           5.83190233e-03,  9.79041126e-03,  1.17874685e-02,  1.17319070e-02,\n",
       "                           9.56096209e-03,  5.24133476e-03, -1.23012116e-03, -9.82609649e-03,\n",
       "                          -2.04889148e-02, -3.31310112e-02, -4.76357970e-02, -6.38589007e-02]),\n",
       "              'yaxis': 'y4'}],\n",
       "    'layout': {'autosize': True,\n",
       "               'font': {'size': 10},\n",
       "               'height': 840,\n",
       "               'hovermode': 'x unified',\n",
       "               'margin': {'b': 0, 'l': 0, 'pad': 0, 'r': 10, 't': 10},\n",
       "               'showlegend': False,\n",
       "               'template': '...',\n",
       "               'title': {'font': {'size': 12}},\n",
       "               'width': 700,\n",
       "               'xaxis': {'anchor': 'y',\n",
       "                         'domain': [0.0, 1.0],\n",
       "                         'linewidth': 1.5,\n",
       "                         'mirror': True,\n",
       "                         'range': [2008-03-18 00:00:00, 2014-07-06 00:00:00],\n",
       "                         'showline': True,\n",
       "                         'title': {'text': 'ds'},\n",
       "                         'type': 'date'},\n",
       "               'xaxis2': {'anchor': 'y2',\n",
       "                          'domain': [0.0, 1.0],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'range': [2008-01-30 22:15:16.363636, 2014-08-22\n",
       "                                    01:44:43.636364],\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'Trend segment'},\n",
       "                          'type': 'date'},\n",
       "               'xaxis3': {'anchor': 'y3',\n",
       "                          'domain': [0.0, 1.0],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'range': [2016-12-14 00:00:00, 2018-01-18 00:00:00],\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'Day of year'}},\n",
       "               'xaxis4': {'anchor': 'y4',\n",
       "                          'domain': [0.0, 1.0],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'range': [-8, 175],\n",
       "                          'showline': True,\n",
       "                          'tickmode': 'array',\n",
       "                          'ticktext': [Sunday, Monday, Tuesday, Wednesday,\n",
       "                                       Thursday, Friday, Saturday, Sunday, Sunday],\n",
       "                          'tickvals': [0, 24, 48, 72, 96, 120, 144, 168, 192],\n",
       "                          'title': {'text': 'Day of week'}},\n",
       "               'yaxis': {'anchor': 'x',\n",
       "                         'domain': [0.80625, 1.0],\n",
       "                         'linewidth': 1.5,\n",
       "                         'mirror': True,\n",
       "                         'rangemode': 'normal',\n",
       "                         'showline': True,\n",
       "                         'title': {'text': 'Trend'}},\n",
       "               'yaxis2': {'anchor': 'x2',\n",
       "                          'domain': [0.5375, 0.73125],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'rangemode': 'normal',\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'Trend Rate Change'}},\n",
       "               'yaxis3': {'anchor': 'x3',\n",
       "                          'domain': [0.26875, 0.4625],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'rangemode': 'normal',\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'Seasonality: yearly'}},\n",
       "               'yaxis4': {'anchor': 'x4',\n",
       "                          'domain': [0.0, 0.19375],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'rangemode': 'normal',\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'Seasonality: weekly'}}}\n",
       "})"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.plot_parameters()\n"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "id": "9912bbb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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