Forecasting every Chicago "Side" #7 Holt Winters and ARIMA with functions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "\n",
    "\n",
    "from plotly import __version__\n",
    "import cufflinks as cf\n",
    "\n",
    "from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot\n",
    "init_notebook_mode(connected=True)\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"seaborn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FNS</th>\n",
       "      <th>Ohara</th>\n",
       "      <th>NWS</th>\n",
       "      <th>NS</th>\n",
       "      <th>WS</th>\n",
       "      <th>CEN</th>\n",
       "      <th>SS</th>\n",
       "      <th>SWS</th>\n",
       "      <th>FSWS</th>\n",
       "      <th>FSES</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10224</td>\n",
       "      <td>10224</td>\n",
       "      <td>10224</td>\n",
       "      <td>10224</td>\n",
       "      <td>10224</td>\n",
       "      <td>10224</td>\n",
       "      <td>10224</td>\n",
       "      <td>10224</td>\n",
       "      <td>10224</td>\n",
       "      <td>10224</td>\n",
       "      <td>10224</td>\n",
       "      <td>10224</td>\n",
       "      <td>10224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>88</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>199</td>\n",
       "      <td>218</td>\n",
       "      <td>308</td>\n",
       "      <td>68</td>\n",
       "      <td>49</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>46</td>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>135</td>\n",
       "      <td>126</td>\n",
       "      <td>196</td>\n",
       "      <td>36</td>\n",
       "      <td>23</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>53</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>101</td>\n",
       "      <td>114</td>\n",
       "      <td>138</td>\n",
       "      <td>37</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>87</td>\n",
       "      <td>36</td>\n",
       "      <td>32</td>\n",
       "      <td>169</td>\n",
       "      <td>219</td>\n",
       "      <td>305</td>\n",
       "      <td>70</td>\n",
       "      <td>51</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>117</td>\n",
       "      <td>61</td>\n",
       "      <td>43</td>\n",
       "      <td>262</td>\n",
       "      <td>298</td>\n",
       "      <td>441</td>\n",
       "      <td>96</td>\n",
       "      <td>67</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>306</td>\n",
       "      <td>168</td>\n",
       "      <td>121</td>\n",
       "      <td>901</td>\n",
       "      <td>738</td>\n",
       "      <td>1079</td>\n",
       "      <td>210</td>\n",
       "      <td>141</td>\n",
       "      <td>80</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         FNS  Ohara    NWS     NS     WS    CEN     SS    SWS   FSWS   FSES  \\\n",
       "count  10224  10224  10224  10224  10224  10224  10224  10224  10224  10224   \n",
       "mean      88     40     32    199    218    308     68     49     22     15   \n",
       "std       46     30     16    135    126    196     36     23     11      8   \n",
       "min        0      0      0      0      0      0      0      0      0      0   \n",
       "25%       53     16     20    101    114    138     37     31     14      9   \n",
       "50%       87     36     32    169    219    305     70     51     23     15   \n",
       "75%      117     61     43    262    298    441     96     67     31     21   \n",
       "max      306    168    121    901    738   1079    210    141     80     53   \n",
       "\n",
       "       is_holiday  is_weekend  season  \n",
       "count       10224       10224   10224  \n",
       "mean            0           0       1  \n",
       "std             0           0       1  \n",
       "min             0           0       0  \n",
       "25%             0           0       1  \n",
       "50%             0           0       2  \n",
       "75%             0           1       2  \n",
       "max             1           1       3  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import hourly pickups by Chicago Side\n",
    "df_sides = pd.read_csv(\"hour_pickups_sides_final.csv\")\n",
    "\n",
    "df_sides[\"StartTime\"] = pd.to_datetime(df_sides[\"StartTime\"], \n",
    "                                       format=\"%d/%m/%Y %H:%M\")\n",
    "\n",
    "# set index\n",
    "df_sides.set_index(\"StartTime\", inplace=True)\n",
    "\n",
    "df_sides.describe().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FNS</th>\n",
       "      <th>Ohara</th>\n",
       "      <th>NWS</th>\n",
       "      <th>NS</th>\n",
       "      <th>WS</th>\n",
       "      <th>CEN</th>\n",
       "      <th>SS</th>\n",
       "      <th>SWS</th>\n",
       "      <th>FSWS</th>\n",
       "      <th>FSES</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>season</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StartTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:00</th>\n",
       "      <td>70.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 01:00:00</th>\n",
       "      <td>46.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 02:00:00</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 03:00:00</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 04:00:00</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 19:00:00</th>\n",
       "      <td>253.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>637.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 20:00:00</th>\n",
       "      <td>279.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 21:00:00</th>\n",
       "      <td>255.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>513.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 22:00:00</th>\n",
       "      <td>199.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:00:00</th>\n",
       "      <td>148.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10224 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       FNS  Ohara    NWS     NS     WS    CEN     SS    SWS  \\\n",
       "StartTime                                                                     \n",
       "2018-11-01 00:00:00   70.0   15.0   20.0  174.0  134.0  141.0   48.0   25.0   \n",
       "2018-11-01 01:00:00   46.0    5.0    9.0  200.0   94.0  103.0   22.0   16.0   \n",
       "2018-11-01 02:00:00   38.0    1.0   11.0  122.0   67.0   74.0   13.0   16.0   \n",
       "2018-11-01 03:00:00   19.0    1.0   15.0   73.0   51.0   63.0    9.0   13.0   \n",
       "2018-11-01 04:00:00   27.0    0.0   12.0   50.0   50.0   49.0   18.0   22.0   \n",
       "...                    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "2019-12-31 19:00:00  253.0   30.0   79.0  600.0  491.0  637.0  109.0   69.0   \n",
       "2019-12-31 20:00:00  279.0   20.0   82.0  695.0  540.0  647.0  110.0  106.0   \n",
       "2019-12-31 21:00:00  255.0   23.0  121.0  610.0  513.0  669.0  115.0   75.0   \n",
       "2019-12-31 22:00:00  199.0   27.0   76.0  500.0  434.0  525.0  125.0   86.0   \n",
       "2019-12-31 23:00:00  148.0    8.0   62.0  310.0  275.0  310.0   76.0   62.0   \n",
       "\n",
       "                     FSWS  FSES  is_holiday  is_weekend  season  \n",
       "StartTime                                                        \n",
       "2018-11-01 00:00:00  17.0  12.0         0.0         0.0     2.0  \n",
       "2018-11-01 01:00:00  17.0   7.0         0.0         0.0     2.0  \n",
       "2018-11-01 02:00:00  13.0   8.0         0.0         0.0     2.0  \n",
       "2018-11-01 03:00:00  15.0   8.0         0.0         0.0     2.0  \n",
       "2018-11-01 04:00:00  13.0   6.0         0.0         0.0     2.0  \n",
       "...                   ...   ...         ...         ...     ...  \n",
       "2019-12-31 19:00:00  32.0  31.0         1.0         0.0     3.0  \n",
       "2019-12-31 20:00:00  47.0  42.0         1.0         0.0     3.0  \n",
       "2019-12-31 21:00:00  40.0  28.0         1.0         0.0     3.0  \n",
       "2019-12-31 22:00:00  38.0  39.0         1.0         0.0     3.0  \n",
       "2019-12-31 23:00:00  40.0  25.0         1.0         0.0     3.0  \n",
       "\n",
       "[10224 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concert dataframe to floats\n",
    "df_sides.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LSTM Multivariate demand prediction with weather data.\n",
    "Should add weather data to the dataset. In other words, I am making a multivariate time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import hourly weather data\n",
    "\n",
    "weather = pd.read_csv(\"Chicago Ohare hourly weather relevant data.csv\")\n",
    "weather[\"Date\"] = pd.to_datetime(weather[\"Date\"])\n",
    "weather.drop(\"WeatherType\", axis=1, inplace=True)\n",
    "weather[\"Temperature\"] = weather[\"Temperature\"].astype(\"float32\")\n",
    "weather.set_index(\"Date\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.66</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 01:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.66</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Precipitation  WindSpeed  Temperature\n",
       "Date                                                      \n",
       "2018-11-01 00:00:00            0.0       9.66          9.0\n",
       "2018-11-01 01:00:00            0.0       9.66          9.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>FNS</th>\n",
       "      <th>Ohara</th>\n",
       "      <th>NWS</th>\n",
       "      <th>NS</th>\n",
       "      <th>WS</th>\n",
       "      <th>CEN</th>\n",
       "      <th>SS</th>\n",
       "      <th>SWS</th>\n",
       "      <th>FSWS</th>\n",
       "      <th>FSES</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>season</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StartTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:00</th>\n",
       "      <td>70</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>174</td>\n",
       "      <td>134</td>\n",
       "      <td>141</td>\n",
       "      <td>48</td>\n",
       "      <td>25</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>9.66</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 01:00:00</th>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>200</td>\n",
       "      <td>94</td>\n",
       "      <td>103</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.66</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 02:00:00</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>122</td>\n",
       "      <td>67</td>\n",
       "      <td>74</td>\n",
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       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.66</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     FNS  Ohara  NWS   NS   WS  CEN  SS  SWS  FSWS  FSES  \\\n",
       "StartTime                                                                  \n",
       "2018-11-01 00:00:00   70     15   20  174  134  141  48   25    17    12   \n",
       "2018-11-01 01:00:00   46      5    9  200   94  103  22   16    17     7   \n",
       "2018-11-01 02:00:00   38      1   11  122   67   74  13   16    13     8   \n",
       "\n",
       "                     is_holiday  is_weekend  season  Precipitation  WindSpeed  \\\n",
       "StartTime                                                                       \n",
       "2018-11-01 00:00:00           0           0       2            0.0       9.66   \n",
       "2018-11-01 01:00:00           0           0       2            0.0       9.66   \n",
       "2018-11-01 02:00:00           0           0       2            0.0       9.66   \n",
       "\n",
       "                     Temperature  \n",
       "StartTime                         \n",
       "2018-11-01 00:00:00          9.0  \n",
       "2018-11-01 01:00:00          9.0  \n",
       "2018-11-01 02:00:00          9.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sides_weather = df_sides.merge(weather, how=\"left\", left_index=True, right_index=True)\n",
    "df_sides_weather.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of hourly pickups across sides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize(data, colnumber, start_val, end_val):\n",
    "    plt.figure(figsize=(10,5))\n",
    "    plt.plot(data.iloc[start_val:end_val, colnumber])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize(df_sides_weather, colnumber=7, start_val=1000, end_val=1050)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iterate over each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tools.eval_measures import rmse\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from pmdarima.arima import auto_arima\n",
    "from statsmodels.tsa.api import ExponentialSmoothing as es\n",
    "from statsmodels.tsa.api import SimpleExpSmoothing as ses\n",
    "from statsmodels.tsa.api import Holt as holt\n",
    "from pmdarima.arima import auto_arima\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>FNS</th>\n",
       "      <th>Ohara</th>\n",
       "      <th>NWS</th>\n",
       "      <th>NS</th>\n",
       "      <th>WS</th>\n",
       "      <th>CEN</th>\n",
       "      <th>SS</th>\n",
       "      <th>SWS</th>\n",
       "      <th>FSWS</th>\n",
       "      <th>FSES</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>season</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StartTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:00</th>\n",
       "      <td>70</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>174</td>\n",
       "      <td>134</td>\n",
       "      <td>141</td>\n",
       "      <td>48</td>\n",
       "      <td>25</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 01:00:00</th>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>200</td>\n",
       "      <td>94</td>\n",
       "      <td>103</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 02:00:00</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>122</td>\n",
       "      <td>67</td>\n",
       "      <td>74</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     FNS  Ohara  NWS   NS   WS  CEN  SS  SWS  FSWS  FSES  \\\n",
       "StartTime                                                                  \n",
       "2018-11-01 00:00:00   70     15   20  174  134  141  48   25    17    12   \n",
       "2018-11-01 01:00:00   46      5    9  200   94  103  22   16    17     7   \n",
       "2018-11-01 02:00:00   38      1   11  122   67   74  13   16    13     8   \n",
       "\n",
       "                     is_holiday  is_weekend  season  \n",
       "StartTime                                            \n",
       "2018-11-01 00:00:00           0           0       2  \n",
       "2018-11-01 01:00:00           0           0       2  \n",
       "2018-11-01 02:00:00           0           0       2  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sides.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1st method: Holt-Winters Exponential Smoothing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions needed to loop through the sides:\n",
    "- Split data\n",
    "- define, fit, and make predictions\n",
    "- Concatenate test set and predictions\n",
    "- Evaluate: calculate RMSE for test data against predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a differenced series\n",
    "def difference(dataset, interval=1):\n",
    "\tdiff = list()\n",
    "\tfor i in range(interval, len(dataset)):\n",
    "\t\tvalue = dataset[i] - dataset[i - interval]\n",
    "\t\tdiff.append(value)\n",
    "\treturn diff\n",
    "\n",
    "# invert differenced value\n",
    "def inverse_difference(history, yhat, interval=1):\n",
    "\treturn yhat + history[-interval]\n",
    "\n",
    "def split_data_hw(data):\n",
    "    train_size = int(len(data) * 0.8)\n",
    "    test_size = len(data) - train_size\n",
    "    \n",
    "    # split data into train and test sets\n",
    "    train = data[0:train_size]\n",
    "    test = data[train_size:]\n",
    "    \n",
    "    return train, test\n",
    "\n",
    "def define_hw(data, s_periods, test):\n",
    "    # Holt-Winters Exponential Smoothing\n",
    "    model = es(data, trend=\"add\", seasonal=\"add\", seasonal_periods=s_periods, damped=True)\n",
    "    # fit model\n",
    "    model_fit = model.fit()\n",
    "    # make predictions\n",
    "    predictions = model_fit.forecast(len(test))\n",
    "    \n",
    "    return model, model_fit, predictions\n",
    "\n",
    "def hw_concat_predictions(predictions, testdata):\n",
    "    # Concatenate predictions on test data with test data set and daily pickups\n",
    "    HW_testpred = pd.DataFrame(predictions)\n",
    "    HW_testpred.rename(columns={0: \"Prediction\"}, inplace=True)\n",
    "    HW_testdata_pred = pd.concat([testdata, HW_testpred], axis=1)\n",
    "    \n",
    "    return HW_testpred, HW_testdata_pred \n",
    "\n",
    "def hw_evaluate(data, start_val, end_val):\n",
    "    # mse\n",
    "    rmse_error = rmse(data.iloc[:, 0], data.iloc[:, -1]) \n",
    "    print(\"RMSE Error:\", rmse_error)\n",
    "\n",
    "    # print min max values of predictions and actual values.\n",
    "    print(\"Min. value test data:\", data.iloc[:, 0].min())\n",
    "    print(\"Max. value test data:\", data.iloc[:, 0].max())\n",
    "\n",
    "    print(\"Min. value predictions:\", data.iloc[:, 1].min())\n",
    "    print(\"Max. value predictions:\", data.iloc[:, 1].max())\n",
    "    \n",
    "    # plot performance\n",
    "#     fig, ax = plt.subplots(figsize=(15,5))\n",
    "#     ax.plot(data.iloc[start_val:end_val, 0])\n",
    "#     ax.plot(data.iloc[start_val:end_val, 1])\n",
    "\n",
    "    return rmse_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data for each Side\n",
    "train_dic = {}\n",
    "test_dic = {}\n",
    "\n",
    "for key in df_sides[[\"FNS\", \"Ohara\", \"NWS\", \"NS\", \"WS\", \"CEN\", \"SS\", \"SWS\", \"FSWS\", \"FSES\"]]:\n",
    "    train, test = split_data_hw(df_sides[key])\n",
    "    train_dic[key], test_dic[key] = train, test\n",
    "    \n",
    "#     train_size = int(len(df_sides[key]) * 0.8)\n",
    "#     test_size = len(df_sides[key]) - train_size\n",
    "    \n",
    "#     # split data into train and test sets\n",
    "#     train = df_sides[key][0:train_size]\n",
    "#     test = df_sides[key][train_size:]\n",
    "#     train_dic[key] = train\n",
    "#     test_dic[key] = test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dic.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Define, fit, make predictions for each side\n",
    "model_dic = {}\n",
    "model_fit_dic = {}\n",
    "predictions_dic = {}\n",
    "\n",
    "for key in train_dic:\n",
    "    model, model_fit, predictions = define_hw(train_dic[key], 168, test_dic[key])\n",
    "    model_dic[key], model_fit_dic[key], predictions_dic[key] = model, model_fit, predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_dic.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concatenate test set and predictions\n",
    "HW_testpred_dic = {}\n",
    "HW_testdata_pred_dic = {}\n",
    "\n",
    "for key in predictions_dic:\n",
    "    HW_testpred, HW_testdata_pred = hw_concat_predictions(predictions_dic[key], test_dic[key])\n",
    "    HW_testpred_dic[key], HW_testdata_pred_dic[key] = HW_testpred, HW_testdata_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(HW_testdata_pred_dic.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# evaluate: calculate RMSE for test data against predictions\n",
    "rmse_error_dic = {}\n",
    "\n",
    "for key in HW_testdata_pred_dic:\n",
    "    rmse_error = hw_evaluate(HW_testdata_pred_dic[key], 1, 10)\n",
    "    rmse_error_dic[key] = rmse_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_error_dic[\"NS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "HW_testdata_pred_dic[\"NS\"].iloc[1500:, 0].plot()\n",
    "HW_testdata_pred_dic[\"NS\"].iloc[1500:, 1].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dic[\"FNS\"].plot()\n",
    "test_dic[\"FNS\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "HW_testdata_pred_dic[\"FNS\"].to_csv(\"p.csv\")\n",
    "HW_testdata_pred_dic[\"Ohara\"].to_csv(\"1.csv\")\n",
    "HW_testdata_pred_dic[\"CEN\"].to_csv(\"2.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2nd method: Seasonal ARIMA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions needed to loop through the sides:\n",
    "- Difference data\n",
    "- define, fit, and make predictions\n",
    "- Concatenate test set and predictions\n",
    "- Inverse differencing\n",
    "- Evaluate: calculate RMSE for test data against predictions\n",
    "\n",
    "SARIMA(p, d, q)x(P, D, Q). Need to determine p, d, q, P, D, Q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Differencing time series of each side"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1st step: plot ACF and PACF plots to estimate d, and D parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = pd.DataFrame(df_sides['Ohara'])\n",
    "df_test[\"station\"] = df_test[\"Ohara\"] - df_test[\"Ohara\"].rolling(24).mean()\n",
    "df_test.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is clearly not stationary given slow decay of spiks in ACF. Need aseasonal and non-seasonal differincing. For now need SARIMA(x, 1, 1)x(x, 1, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(3, figsize=(10, 10))\n",
    "x = (df_test.station.dropna() - df_test.station.dropna().shift(168)).dropna()\n",
    "ax[0] = plot_acf(x, ax=ax[0], lags=25)\n",
    "ax[1] = plot_pacf(x, ax=ax[1], lags=25)\n",
    "ax[2].plot(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fns = df_sides.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stationarity(timeseries):\n",
    "    # rolling statistics\n",
    "    rolling_mean = timeseries.rolling(window=24).mean()\n",
    "    rolling_std = timeseries.rolling(window=24).std()\n",
    "    \n",
    "    # rolling statistics plot\n",
    "    fig, ax = plt.subplots(figsize=(15,5))\n",
    "    ax.plot(timeseries.index, timeseries, color=\"blue\", label=\"Original\")\n",
    "    ax.plot(rolling_mean, color=\"red\", label=\"Rolling Mean\")\n",
    "    ax.plot(rolling_std, color=\"black\", label=\"Rolling Std\")\n",
    "    ax.legend(loc=\"best\")\n",
    "    ax.set_title(\"Rolling Mean & Standard Deviation\")\n",
    "    \n",
    "    # Dickey-Fuller test:\n",
    "    result = adfuller(timeseries, maxlag=24)\n",
    "    print(\"ADF Statistics: {}\".format(result[0]))\n",
    "    print(\"p-value: {}\".format(result[1]))\n",
    "    print(\"Critical Values:\")\n",
    "    for key, value in result[4].items():\n",
    "        print('\\t{}: {}'.format(key, value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_stationarity(diff_values) # ohara"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# get ACF and PACF plot\n",
    "plot_acf(df_fns, alpha=0.01)\n",
    "plot_pacf(df_fns, alpha=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ACF function shows slow decline of the correlation between lags, indicative that many lags can be used to forecast future events, but this is just the nature of non-stationary series.\n",
    "\n",
    "PCF shows indirect correlation by removing the effect of preceding lags. Only the first two lags are useful. \n",
    "\n",
    "When differncing the time series, there is only a significant correlation in the first 3 lags with the first lag being a strong predictor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10223, 1)\n"
     ]
    }
   ],
   "source": [
    "# select Side\n",
    "df_fns = df_sides.iloc[:,5]\n",
    "\n",
    "# difference and split data\n",
    "train_fns, test_fns = split_data_hw(df_fns)\n",
    "diff_values = pd.DataFrame(difference(df_fns, 1))\n",
    "print(diff_values.shape)\n",
    "\n",
    "train_fns_diff, test_fns_diff = split_data_hw(diff_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get ACF and PACF plot\n",
    "plot_acf(diff_values, alpha=0.01)\n",
    "plot_pacf(diff_values, alpha=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12b296190>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff_values.iloc[1:10000, 0].plot(figsize=(20,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARIMA: auto arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      "Fit ARIMA(0,0,0)x(0,0,0,0) [intercept=True]; AIC=94370.554, BIC=94384.573, Time=0.274 seconds\n",
      "Fit ARIMA(1,0,0)x(0,0,0,0) [intercept=True]; AIC=92864.186, BIC=92885.213, Time=0.286 seconds\n",
      "Fit ARIMA(0,0,1)x(0,0,0,0) [intercept=True]; AIC=92880.771, BIC=92901.798, Time=0.936 seconds\n",
      "Fit ARIMA(0,0,0)x(0,0,0,0) [intercept=False]; AIC=94368.557, BIC=94375.566, Time=0.073 seconds\n",
      "Fit ARIMA(2,0,0)x(0,0,0,0) [intercept=True]; AIC=92805.878, BIC=92833.915, Time=0.395 seconds\n",
      "Fit ARIMA(3,0,0)x(0,0,0,0) [intercept=True]; AIC=92800.305, BIC=92835.351, Time=0.514 seconds\n",
      "Fit ARIMA(3,0,1)x(0,0,0,0) [intercept=True]; AIC=92802.296, BIC=92844.351, Time=2.681 seconds\n",
      "Fit ARIMA(2,0,1)x(0,0,0,0) [intercept=True]; AIC=92802.734, BIC=92837.780, Time=1.291 seconds\n",
      "Total fit time: 6.460 seconds\n"
     ]
    }
   ],
   "source": [
    "# define model\n",
    "stepwise_model = auto_arima(train_fns_diff, m=168, \n",
    "                            start_p=0, max_d=2, start_q=0,\n",
    "                            max_p=3, max_q=3, start_P=0,\n",
    "                            start_Q=0, max_D=1, max_P=2,\n",
    "                            max_Q=2, trace=True, seasonal=False, \n",
    "                            error_action=\"ignore\", suppress_warnings=True, stepwise=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>8178</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(3, 0, 0)</td> <th>  Log Likelihood     </th> <td>-46395.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Fri, 26 Jun 2020</td> <th>  AIC                </th>  <td>92800.305</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>19:55:20</td>     <th>  BIC                </th>  <td>92835.351</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th>  <td>92812.287</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 8178</td>     <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td>    0.0344</td> <td>    0.859</td> <td>    0.040</td> <td> 0.968</td> <td>   -1.649</td> <td>    1.717</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>    0.4482</td> <td>    0.009</td> <td>   48.721</td> <td> 0.000</td> <td>    0.430</td> <td>    0.466</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>     <td>   -0.0993</td> <td>    0.010</td> <td>   -9.709</td> <td> 0.000</td> <td>   -0.119</td> <td>   -0.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3</th>     <td>    0.0304</td> <td>    0.011</td> <td>    2.761</td> <td> 0.006</td> <td>    0.009</td> <td>    0.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td> 4958.0272</td> <td>   60.100</td> <td>   82.496</td> <td> 0.000</td> <td> 4840.233</td> <td> 5075.821</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>5055.05</td> <th>  Jarque-Bera (JB):  </th> <td>1325.27</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                 <td>0.00</td>   <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th>  <td>1.53</td>   <th>  Skew:              </th>  <td>-0.41</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>     <td>0.00</td>   <th>  Kurtosis:          </th>  <td>4.80</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                 8178\n",
       "Model:               SARIMAX(3, 0, 0)   Log Likelihood              -46395.152\n",
       "Date:                Fri, 26 Jun 2020   AIC                          92800.305\n",
       "Time:                        19:55:20   BIC                          92835.351\n",
       "Sample:                             0   HQIC                         92812.287\n",
       "                               - 8178                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept      0.0344      0.859      0.040      0.968      -1.649       1.717\n",
       "ar.L1          0.4482      0.009     48.721      0.000       0.430       0.466\n",
       "ar.L2         -0.0993      0.010     -9.709      0.000      -0.119      -0.079\n",
       "ar.L3          0.0304      0.011      2.761      0.006       0.009       0.052\n",
       "sigma2      4958.0272     60.100     82.496      0.000    4840.233    5075.821\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                     5055.05   Jarque-Bera (JB):              1325.27\n",
       "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               1.53   Skew:                            -0.41\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         4.80\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stepwise_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2045\n"
     ]
    }
   ],
   "source": [
    "# forecast\n",
    "n_test = len(test_fns)\n",
    "test_predictions = stepwise_model.predict(n_test)\n",
    "print(len(test_predictions))\n",
    "\n",
    "# invert predictions\n",
    "inverted_ytest = list()\n",
    "for i in range(len(test_predictions)):\n",
    "    value = inverse_difference(df_fns, test_predictions[i], len(test_predictions)+1-i) # 10224, 2045, 2045+1-i\n",
    "    inverted_ytest.append(value)\n",
    "inverted_ytest = pd.DataFrame(inverted_ytest)\n",
    "inverted_ytest\n",
    "\n",
    "# concat predictions\n",
    "testdata_pred = pd.concat([test_fns.reset_index().iloc[:,1:], inverted_ytest], axis=1)\n",
    "\n",
    "sarima_testpred = pd.DataFrame(inverted_ytest)\n",
    "sarima_testpred.rename(columns={0: \"Prediction\"}, inplace=True)\n",
    "sarima_testdata_pred = pd.concat([test_dic[\"CEN\"].reset_index().iloc[:,1:], sarima_testpred], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>CEN</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>504</td>\n",
       "      <td>511.865591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>424</td>\n",
       "      <td>501.900589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>345</td>\n",
       "      <td>424.756456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>276</td>\n",
       "      <td>345.090783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>147</td>\n",
       "      <td>275.936034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2040</th>\n",
       "      <td>637</td>\n",
       "      <td>496.055363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2041</th>\n",
       "      <td>647</td>\n",
       "      <td>637.055363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2042</th>\n",
       "      <td>669</td>\n",
       "      <td>647.055363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2043</th>\n",
       "      <td>525</td>\n",
       "      <td>669.055363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2044</th>\n",
       "      <td>310</td>\n",
       "      <td>525.055363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2045 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CEN  Prediction\n",
       "0     504  511.865591\n",
       "1     424  501.900589\n",
       "2     345  424.756456\n",
       "3     276  345.090783\n",
       "4     147  275.936034\n",
       "...   ...         ...\n",
       "2040  637  496.055363\n",
       "2041  647  637.055363\n",
       "2042  669  647.055363\n",
       "2043  525  669.055363\n",
       "2044  310  525.055363\n",
       "\n",
       "[2045 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sarima_testdata_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.27615204487256"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_error = rmse(sarima_testdata_pred.iloc[1:, 0], sarima_testdata_pred.iloc[1:, -1]) \n",
    "rmse_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12b020510>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sarima_testdata_pred.iloc[100:200, 0].plot()\n",
    "sarima_testdata_pred.iloc[100:200, 1].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARIMA: SARIMAX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_order = (1,0,0)\n",
    "my_seasonal_order = (0,1,1,24)\n",
    "\n",
    "model = SARIMAX(train_fns_diff, \n",
    "                order=my_order, trend=\"c\",\n",
    "                seasonal_order=my_seasonal_order, \n",
    "                enforce_stationarity=True,\n",
    "                enforce_invertibility=True).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# forecast\n",
    "n_test = len(test_fns)\n",
    "test_predictions = model.forecast(n_test)\n",
    "test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# invert predictions\n",
    "inverted_ytest = list()\n",
    "for i in range(len(test_predictions)):\n",
    "    value = test_predictions.iloc[i] + df_fns.iloc[-(len(test_predictions)+1-i)]\n",
    "    inverted_ytest.append(value)\n",
    "inverted_ytest = pd.DataFrame(inverted_ytest)\n",
    "\n",
    "# inverse_difference(df_fns, test_predictions[i], len(test_predictions)+1-i) # 10224, 2045, 2045+1-i\n",
    "inverted_ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sarima_testpred = pd.DataFrame(inverted_ytest)\n",
    "sarima_testpred.rename(columns={0: \"Prediction\"}, inplace=True)\n",
    "sarima_testdata_pred = pd.concat([test_dic[\"FNS\"].reset_index().iloc[:,1:], sarima_testpred], axis=1)\n",
    "sarima_testdata_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_error = rmse(sarima_testdata_pred.iloc[1:, 0], sarima_testdata_pred.iloc[1:, -1]) \n",
    "rmse_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sarima_testdata_pred.iloc[1500:-1, 0].plot(figsize=(15,8))\n",
    "sarima_testdata_pred.iloc[1500:-1, 1].plot(figsize=(15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define, fit, make predictions for each side\n",
    "model_dic = {}\n",
    "test_predictions_dic = {}\n",
    "\n",
    "for key in train_dic:\n",
    "    model, test_predictions = define_sarima(train_dic[key], my_order, my_seasonal_order, test_dic[key])\n",
    "    model_dic[key], test_predictions_dic[key] = model, test_predictions\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concatenate test set and predictions\n",
    "sarima_testpred_dic = {}\n",
    "sarima_testdata_pred_dic = {}\n",
    "\n",
    "for key in test_predictions_dic:\n",
    "    sarima_testpred, sarima_testdata_pred = sarima_concatenate_predictions(test_predictions_dic[key], test_dic[key])\n",
    "    sarima_testpred_dic[key], sarima_testdata_pred[key] = sarima_testpred, sarima_testdata_pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluate: calculate RMSE for test data against predictions\n",
    "rmse_error_dic = {}\n",
    "\n",
    "for key in sarima_testdata_pred_dic:\n",
    "    rmse_error = sarima_evaluate(sarima_testdata_pred[key], 1, 10)\n",
    "    rmse_error_dic = rmse_error\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3rd method: Prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a new df for prophet test\n",
    "\n",
    "df_test = df_sides[\"CEN\"]\n",
    "df_test[\"ds\"] = df_test.index\n",
    "df_test[\"y\"] = df_test[\"CEN\"]\n",
    "df_test.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fitting df to prophet\n",
    "\n",
    "model = Prohet(seasonality=True)\n",
    "model.fit(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "future = model.make_future_dataframe(periodes=X, freq=\"H\")\n",
    "\n",
    "# predict method assigns to each row a predicted value named yhat.\n",
    "# also has yhat_lower and yhat_upper as confidence intervals.\n",
    "forecast = model.predict(future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# forecasting\n",
    "model.plot(forecast, xlabel=\"Date\", ylabel=\"Hourly pickups\")\n",
    "plt.title('Prophet - Model fit and 1-month prediction')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.plot_components(forecast);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scoring\n",
    "predictions = pd.DataFrame(forecast.yhat[-720:])\n",
    "plot_forecast(test[\"columnname\"], predictions)\n",
    "rmse(ts15_test[\"columnname\"], predictions)"
   ]
  }
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