arimavariations_autoarima_istanbul.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pmdarima.arima import auto_arima\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>_id</th>\n",
       "      <th>DATE_TIME</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>GEOHASH</th>\n",
       "      <th>MINIMUM_SPEED</th>\n",
       "      <th>MAXIMUM_SPEED</th>\n",
       "      <th>AVERAGE_SPEED</th>\n",
       "      <th>NUMBER_OF_VEHICLES</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>12913</td>\n",
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       "      <td>sxk9wk</td>\n",
       "      <td>58</td>\n",
       "      <td>144</td>\n",
       "      <td>94</td>\n",
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       "      <th>1</th>\n",
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       "      <td>2020-01-01 01:00:00</td>\n",
       "      <td>29.108276</td>\n",
       "      <td>41.069641</td>\n",
       "      <td>sxk9wk</td>\n",
       "      <td>64</td>\n",
       "      <td>136</td>\n",
       "      <td>95</td>\n",
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       "      <th>2</th>\n",
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       "      <td>160510</td>\n",
       "      <td>2020-01-01 02:00:00</td>\n",
       "      <td>29.108276</td>\n",
       "      <td>41.069641</td>\n",
       "      <td>sxk9wk</td>\n",
       "      <td>68</td>\n",
       "      <td>181</td>\n",
       "      <td>105</td>\n",
       "      <td>97</td>\n",
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       "      <th>3</th>\n",
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       "      <td>136168</td>\n",
       "      <td>2020-01-01 03:00:00</td>\n",
       "      <td>29.108276</td>\n",
       "      <td>41.069641</td>\n",
       "      <td>sxk9wk</td>\n",
       "      <td>76</td>\n",
       "      <td>158</td>\n",
       "      <td>107</td>\n",
       "      <td>63</td>\n",
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       "      <th>4</th>\n",
       "      <td>684343</td>\n",
       "      <td>684344</td>\n",
       "      <td>2020-01-01 04:00:00</td>\n",
       "      <td>29.108276</td>\n",
       "      <td>41.069641</td>\n",
       "      <td>sxk9wk</td>\n",
       "      <td>61</td>\n",
       "      <td>196</td>\n",
       "      <td>102</td>\n",
       "      <td>53</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>11060</th>\n",
       "      <td>1179972</td>\n",
       "      <td>1179973</td>\n",
       "      <td>2021-04-30 18:00:00</td>\n",
       "      <td>29.108276</td>\n",
       "      <td>41.069641</td>\n",
       "      <td>sxk9wk</td>\n",
       "      <td>50</td>\n",
       "      <td>170</td>\n",
       "      <td>97</td>\n",
       "      <td>273</td>\n",
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       "    <tr>\n",
       "      <th>11061</th>\n",
       "      <td>1197195</td>\n",
       "      <td>1197196</td>\n",
       "      <td>2021-04-30 19:00:00</td>\n",
       "      <td>29.108276</td>\n",
       "      <td>41.069641</td>\n",
       "      <td>sxk9wk</td>\n",
       "      <td>58</td>\n",
       "      <td>177</td>\n",
       "      <td>109</td>\n",
       "      <td>198</td>\n",
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       "      <td>551362</td>\n",
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       "      <td>2021-04-30 20:00:00</td>\n",
       "      <td>29.108276</td>\n",
       "      <td>41.069641</td>\n",
       "      <td>sxk9wk</td>\n",
       "      <td>64</td>\n",
       "      <td>164</td>\n",
       "      <td>113</td>\n",
       "      <td>139</td>\n",
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       "      <th>11063</th>\n",
       "      <td>444616</td>\n",
       "      <td>444617</td>\n",
       "      <td>2021-04-30 21:00:00</td>\n",
       "      <td>29.108276</td>\n",
       "      <td>41.069641</td>\n",
       "      <td>sxk9wk</td>\n",
       "      <td>64</td>\n",
       "      <td>182</td>\n",
       "      <td>100</td>\n",
       "      <td>128</td>\n",
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       "      <th>11064</th>\n",
       "      <td>914968</td>\n",
       "      <td>914969</td>\n",
       "      <td>2021-04-30 22:00:00</td>\n",
       "      <td>29.108276</td>\n",
       "      <td>41.069641</td>\n",
       "      <td>sxk9wk</td>\n",
       "      <td>50</td>\n",
       "      <td>168</td>\n",
       "      <td>102</td>\n",
       "      <td>109</td>\n",
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       "<p>11065 rows × 10 columns</p>\n",
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      ],
      "text/plain": [
       "       Unnamed: 0      _id            DATE_TIME  LONGITUDE   LATITUDE GEOHASH  \\\n",
       "0           12913    12914  2020-01-01 00:00:00  29.108276  41.069641  sxk9wk   \n",
       "1          621904   621905  2020-01-01 01:00:00  29.108276  41.069641  sxk9wk   \n",
       "2          160509   160510  2020-01-01 02:00:00  29.108276  41.069641  sxk9wk   \n",
       "3          136167   136168  2020-01-01 03:00:00  29.108276  41.069641  sxk9wk   \n",
       "4          684343   684344  2020-01-01 04:00:00  29.108276  41.069641  sxk9wk   \n",
       "...           ...      ...                  ...        ...        ...     ...   \n",
       "11060     1179972  1179973  2021-04-30 18:00:00  29.108276  41.069641  sxk9wk   \n",
       "11061     1197195  1197196  2021-04-30 19:00:00  29.108276  41.069641  sxk9wk   \n",
       "11062      551362   551363  2021-04-30 20:00:00  29.108276  41.069641  sxk9wk   \n",
       "11063      444616   444617  2021-04-30 21:00:00  29.108276  41.069641  sxk9wk   \n",
       "11064      914968   914969  2021-04-30 22:00:00  29.108276  41.069641  sxk9wk   \n",
       "\n",
       "       MINIMUM_SPEED  MAXIMUM_SPEED  AVERAGE_SPEED  NUMBER_OF_VEHICLES  \n",
       "0                 58            144             94                 114  \n",
       "1                 64            136             95                 105  \n",
       "2                 68            181            105                  97  \n",
       "3                 76            158            107                  63  \n",
       "4                 61            196            102                  53  \n",
       "...              ...            ...            ...                 ...  \n",
       "11060             50            170             97                 273  \n",
       "11061             58            177            109                 198  \n",
       "11062             64            164            113                 139  \n",
       "11063             64            182            100                 128  \n",
       "11064             50            168            102                 109  \n",
       "\n",
       "[11065 rows x 10 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/istanbul/istanbul_data_del.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = data[\"NUMBER_OF_VEHICLES\"].values[:-3500]\n",
    "test_data = data[\"NUMBER_OF_VEHICLES\"].values[-3500:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(1,0,0)(0,0,0)[0]             : AIC=84063.193, Time=2.02 sec\n",
      " ARIMA(0,0,0)(0,0,0)[0]             : AIC=103809.100, Time=0.09 sec\n",
      " ARIMA(2,0,0)(0,0,0)[0]             : AIC=83822.416, Time=0.31 sec\n",
      " ARIMA(3,0,0)(0,0,0)[0]             : AIC=83816.720, Time=0.39 sec\n",
      " ARIMA(4,0,0)(0,0,0)[0]             : AIC=83812.823, Time=0.47 sec\n",
      " ARIMA(5,0,0)(0,0,0)[0]             : AIC=83777.511, Time=0.65 sec\n",
      " ARIMA(5,0,0)(0,0,0)[0] intercept   : AIC=83055.995, Time=1.49 sec\n",
      " ARIMA(4,0,0)(0,0,0)[0] intercept   : AIC=83059.645, Time=1.22 sec\n",
      "\n",
      "Best model:  ARIMA(5,0,0)(0,0,0)[0] intercept\n",
      "Total fit time: 6.632 seconds\n"
     ]
    }
   ],
   "source": [
    "model = auto_arima(train_data, start_p=1, start_q=0,\n",
    "                      max_p=5, max_q=0,            \n",
    "                      d=0,          \n",
    "                      seasonal=False,   \n",
    "                      start_P=0, \n",
    "                      D=None, \n",
    "                      trace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(1,0,1)(0,0,0)[0]             : AIC=83816.739, Time=0.45 sec\n",
      " ARIMA(0,0,0)(0,0,0)[0]             : AIC=103809.100, Time=0.08 sec\n",
      " ARIMA(1,0,0)(0,0,0)[0]             : AIC=84063.193, Time=0.16 sec\n",
      " ARIMA(0,0,1)(0,0,0)[0]             : AIC=95706.917, Time=0.68 sec\n",
      " ARIMA(1,0,1)(0,0,0)[0] intercept   : AIC=83196.692, Time=2.42 sec\n",
      " ARIMA(0,0,1)(0,0,0)[0] intercept   : AIC=87293.626, Time=1.39 sec\n",
      " ARIMA(1,0,0)(0,0,0)[0] intercept   : AIC=83609.892, Time=0.41 sec\n",
      " ARIMA(0,0,0)(0,0,0)[0] intercept   : AIC=93226.640, Time=0.12 sec\n",
      "\n",
      "Best model:  ARIMA(1,0,1)(0,0,0)[0] intercept\n",
      "Total fit time: 5.717 seconds\n"
     ]
    }
   ],
   "source": [
    "model = auto_arima(train_data, start_p=1, start_q=1,\n",
    "                      max_p=1, max_q=1,            \n",
    "                      d=0,          \n",
    "                      seasonal=False,   \n",
    "                      start_P=0, \n",
    "                      D=None, \n",
    "                      trace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=83994.220, Time=0.93 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=84192.277, Time=0.16 sec\n",
      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=84008.590, Time=0.41 sec\n",
      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=83993.884, Time=0.71 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=84190.279, Time=0.09 sec\n",
      " ARIMA(0,1,2)(0,0,0)[0] intercept   : AIC=83993.439, Time=0.72 sec\n",
      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=7.01 sec\n",
      " ARIMA(0,1,3)(0,0,0)[0] intercept   : AIC=83975.918, Time=1.03 sec\n",
      " ARIMA(1,1,3)(0,0,0)[0] intercept   : AIC=inf, Time=17.39 sec\n",
      " ARIMA(0,1,4)(0,0,0)[0] intercept   : AIC=inf, Time=12.40 sec\n",
      " ARIMA(1,1,4)(0,0,0)[0] intercept   : AIC=inf, Time=10.19 sec\n",
      " ARIMA(0,1,3)(0,0,0)[0]             : AIC=83973.919, Time=0.64 sec\n",
      " ARIMA(0,1,2)(0,0,0)[0]             : AIC=83991.440, Time=0.46 sec\n",
      " ARIMA(1,1,3)(0,0,0)[0]             : AIC=inf, Time=6.96 sec\n",
      " ARIMA(0,1,4)(0,0,0)[0]             : AIC=inf, Time=6.19 sec\n",
      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=inf, Time=4.73 sec\n",
      " ARIMA(1,1,4)(0,0,0)[0]             : AIC=inf, Time=13.72 sec\n",
      "\n",
      "Best model:  ARIMA(0,1,3)(0,0,0)[0]          \n",
      "Total fit time: 83.753 seconds\n"
     ]
    }
   ],
   "source": [
    "model = auto_arima(train_data, start_p=1, start_q=1,\n",
    "                      max_p=5, max_q=5,            \n",
    "                      d=1,          \n",
    "                      seasonal=False,   \n",
    "                      start_P=0, \n",
    "                      D=None, \n",
    "                      trace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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