Arima_Test_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4ae6e97c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pmdarima in c:\\users\\aaron\\anaconda3\\envs\\mlenv\\lib\\site-packages (1.8.5)\n",
      "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in c:\\users\\aaron\\anaconda3\\envs\\mlenv\\lib\\site-packages (from pmdarima) (58.0.4)\n",
      "Requirement already satisfied: scikit-learn>=0.22 in c:\\users\\aaron\\anaconda3\\envs\\mlenv\\lib\\site-packages (from pmdarima) (1.0.2)\n",
      "Requirement already satisfied: numpy>=1.19.3 in c:\\users\\aaron\\anaconda3\\envs\\mlenv\\lib\\site-packages (from pmdarima) (1.20.3)\n",
      "Requirement already satisfied: Cython!=0.29.18,>=0.29 in c:\\users\\aaron\\anaconda3\\envs\\mlenv\\lib\\site-packages (from pmdarima) (0.29.25)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\aaron\\anaconda3\\envs\\mlenv\\lib\\site-packages (from pmdarima) (1.7.3)\n",
      "Requirement already satisfied: statsmodels!=0.12.0,>=0.11 in c:\\users\\aaron\\anaconda3\\envs\\mlenv\\lib\\site-packages (from pmdarima) (0.12.2)\n",
      "Requirement already satisfied: pandas>=0.19 in c:\\users\\aaron\\anaconda3\\envs\\mlenv\\lib\\site-packages (from pmdarima) (1.3.5)\n",
      "Requirement already satisfied: urllib3 in c:\\users\\aaron\\anaconda3\\envs\\mlenv\\lib\\site-packages (from pmdarima) (1.26.7)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\aaron\\anaconda3\\envs\\mlenv\\lib\\site-packages (from pmdarima) (1.1.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\aaron\\anaconda3\\envs\\mlenv\\lib\\site-packages (from pandas>=0.19->pmdarima) (2021.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\aaron\\anaconda3\\envs\\mlenv\\lib\\site-packages (from pandas>=0.19->pmdarima) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\aaron\\anaconda3\\envs\\mlenv\\lib\\site-packages (from python-dateutil>=2.7.3->pandas>=0.19->pmdarima) (1.16.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\aaron\\anaconda3\\envs\\mlenv\\lib\\site-packages (from scikit-learn>=0.22->pmdarima) (2.2.0)\n",
      "Requirement already satisfied: patsy>=0.5 in c:\\users\\aaron\\anaconda3\\envs\\mlenv\\lib\\site-packages (from statsmodels!=0.12.0,>=0.11->pmdarima) (0.5.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pmdarima"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435870e7",
   "metadata": {},
   "source": [
    "### Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "836da348",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data (264, 11)\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>Rate_High_School</th>\n",
       "      <th>Rate_Bachelors</th>\n",
       "      <th>Rate_Masters</th>\n",
       "      <th>Rate_Doctoral</th>\n",
       "      <th>Rate_Men</th>\n",
       "      <th>Rate_Women</th>\n",
       "      <th>Rate_Asian</th>\n",
       "      <th>Rate_Black</th>\n",
       "      <th>Rate_White</th>\n",
       "      <th>Rate_Latino</th>\n",
       "      <th>Rate_Overall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</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>2000-01-01</th>\n",
       "      <td>3.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-01</th>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-01</th>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-01</th>\n",
       "      <td>3.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05-01</th>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rate_High_School  Rate_Bachelors  Rate_Masters  Rate_Doctoral  \\\n",
       "DATE                                                                        \n",
       "2000-01-01               3.4             1.8           1.6            1.4   \n",
       "2000-02-01               3.4             1.6           1.6            1.4   \n",
       "2000-03-01               3.3             1.7           1.7            0.9   \n",
       "2000-04-01               3.3             1.5           1.2            0.6   \n",
       "2000-05-01               3.4             1.6           1.0            0.8   \n",
       "\n",
       "            Rate_Men  Rate_Women  Rate_Asian  Rate_Black  Rate_White  \\\n",
       "DATE                                                                   \n",
       "2000-01-01       3.9         4.1         4.0         8.2         3.4   \n",
       "2000-02-01       4.1         4.1         3.4         8.1         3.6   \n",
       "2000-03-01       3.8         4.3         4.1         7.4         3.5   \n",
       "2000-04-01       3.7         4.0         3.1         7.0         3.4   \n",
       "2000-05-01       3.9         4.2         3.5         7.7         3.5   \n",
       "\n",
       "            Rate_Latino  Rate_Overall  \n",
       "DATE                                   \n",
       "2000-01-01          5.6           4.0  \n",
       "2000-02-01          5.7           4.1  \n",
       "2000-03-01          6.1           4.0  \n",
       "2000-04-01          5.5           3.8  \n",
       "2000-05-01          5.8           4.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# line plot of time series\n",
    "import pandas as pd\n",
    "from pandas import read_csv\n",
    "from matplotlib import pyplot\n",
    "import numpy as np\n",
    "# load dataset\n",
    "df = pd.read_csv('Resources/full.csv', header=0, index_col='DATE', parse_dates=True)\n",
    "df=df.dropna()\n",
    "print('Shape of data', df.shape)\n",
    "\n",
    "# display first few rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b818833",
   "metadata": {},
   "source": [
    "### Plot Your Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7db94af6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Rate_Overall'].plot(figsize=(12,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4472909c",
   "metadata": {},
   "source": [
    "### Check for Stationarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3eff08b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d26603fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ad_test(dataset):\n",
    "    dftest = adfuller(dataset, autolag = 'AIC')\n",
    "    print(\"1. ADF : \", dftest[0])\n",
    "    print(\"2. P-Value : \", dftest[1])\n",
    "    print(\"3. Num of Lags : \", dftest[2])\n",
    "    print(\"4. Num Of Observations Used For ADF Regression and Critical Values Calculation :\", dftest[3])\n",
    "    print(\"5. Critical Values :\")\n",
    "    for key, val in dftest[4].items():\n",
    "        print(\"\\t\",key, \": \", val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "85f66b9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. ADF :  -2.92252167817022\n",
      "2. P-Value :  0.04279101681629297\n",
      "3. Num of Lags :  0\n",
      "4. Num Of Observations Used For ADF Regression and Critical Values Calculation : 263\n",
      "5. Critical Values :\n",
      "\t 1% :  -3.4554613060274972\n",
      "\t 5% :  -2.8725931472675046\n",
      "\t 10% :  -2.5726600403359887\n"
     ]
    }
   ],
   "source": [
    "ad_test(df['Rate_Overall'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9124ddc9",
   "metadata": {},
   "source": [
    "### Figure Out Order for ARIMA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b0637b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pmdarima import auto_arima\n",
    "# Ignore harmless warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "214bec8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.32 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=557.473, Time=0.03 sec\n",
      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=559.263, Time=0.03 sec\n",
      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=559.203, Time=0.04 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=555.474, Time=0.01 sec\n",
      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=558.404, Time=0.10 sec\n",
      "\n",
      "Best model:  ARIMA(0,1,0)(0,0,0)[0]          \n",
      "Total fit time: 0.526 seconds\n"
     ]
    },
    {
     "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>264</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(0, 1, 0)</td> <th>  Log Likelihood     </th> <td>-276.737</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sun, 20 Mar 2022</td> <th>  AIC                </th>  <td>555.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>19:57:40</td>     <th>  BIC                </th>  <td>559.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th>  <td>556.909</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 264</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>sigma2</th> <td>    0.4803</td> <td>    0.004</td> <td>  110.357</td> <td> 0.000</td> <td>    0.472</td> <td>    0.489</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.21</td>  <th>  Jarque-Bera (JB):  </th> <td>374579.88</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.64</td>  <th>  Prob(JB):          </th>   <td>0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>89.15</td> <th>  Skew:              </th>   <td>12.25</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td>  <th>  Kurtosis:          </th>  <td>186.25</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:                  264\n",
       "Model:               SARIMAX(0, 1, 0)   Log Likelihood                -276.737\n",
       "Date:                Sun, 20 Mar 2022   AIC                            555.474\n",
       "Time:                        19:57:40   BIC                            559.046\n",
       "Sample:                             0   HQIC                           556.909\n",
       "                                - 264                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         0.4803      0.004    110.357      0.000       0.472       0.489\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.21   Jarque-Bera (JB):            374579.88\n",
       "Prob(Q):                              0.64   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):              89.15   Skew:                            12.25\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                       186.25\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stepwise_fit = auto_arima(df['Rate_Overall'], trace=True,\n",
    "                         suppress_warnings=True)\n",
    "\n",
    "stepwise_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "892125c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33f0f5e9",
   "metadata": {},
   "source": [
    "### Split Data into Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1cd858d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(264, 11)\n",
      "(252, 11) (12, 11)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "train = df.iloc[:-12]\n",
    "test = df.iloc[-12:]\n",
    "print(train.shape,test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14ec9801",
   "metadata": {},
   "source": [
    "### Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4edc8c79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>Rate_Overall</td>   <th>  No. Observations:  </th>    <td>252</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(0, 1, 0)</td>  <th>  Log Likelihood     </th> <td>-268.953</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sun, 20 Mar 2022</td> <th>  AIC                </th>  <td>539.907</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>19:57:40</td>     <th>  BIC                </th>  <td>543.432</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>01-01-2000</td>    <th>  HQIC               </th>  <td>541.326</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 12-01-2020</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>sigma2</th> <td>    0.4992</td> <td>    0.005</td> <td>  106.169</td> <td> 0.000</td> <td>    0.490</td> <td>    0.508</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.16</td>  <th>  Jarque-Bera (JB):  </th> <td>333673.32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.69</td>  <th>  Prob(JB):          </th>   <td>0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>91.33</td> <th>  Skew:              </th>   <td>12.07</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td>  <th>  Kurtosis:          </th>  <td>179.98</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:           Rate_Overall   No. Observations:                  252\n",
       "Model:                 ARIMA(0, 1, 0)   Log Likelihood                -268.953\n",
       "Date:                Sun, 20 Mar 2022   AIC                            539.907\n",
       "Time:                        19:57:40   BIC                            543.432\n",
       "Sample:                    01-01-2000   HQIC                           541.326\n",
       "                         - 12-01-2020                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         0.4992      0.005    106.169      0.000       0.490       0.508\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.16   Jarque-Bera (JB):            333673.32\n",
       "Prob(Q):                              0.69   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):              91.33   Skew:                            12.07\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                       179.98\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=ARIMA(train['Rate_Overall'], order=(0,1,0))\n",
    "model=model.fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed64ac2a",
   "metadata": {},
   "source": [
    "### Make Predictions on Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "253b6bf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATE\n",
      "2021-01-01    6.7\n",
      "2021-02-01    6.7\n",
      "2021-03-01    6.7\n",
      "2021-04-01    6.7\n",
      "2021-05-01    6.7\n",
      "2021-06-01    6.7\n",
      "2021-07-01    6.7\n",
      "2021-08-01    6.7\n",
      "2021-09-01    6.7\n",
      "2021-10-01    6.7\n",
      "2021-11-01    6.7\n",
      "2021-12-01    6.7\n",
      "Name: predicted_mean, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "start = len(train)\n",
    "end = len(train) + len(test) - 1\n",
    "pred = model.predict(start=start, end=end, typ='levels')\n",
    "#print(pred)\n",
    "pred.index=df.index[start:end+1]\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "495d4450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred.plot(legend=True)\n",
    "test['Rate_Overall'].plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51c5d3c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.358333333333334"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Rate_Overall'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "87a734ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5601816133600177\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "rmse=sqrt(mean_squared_error(pred,test['Rate_Overall']))\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3ad8c037",
   "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>Rate_High_School</th>\n",
       "      <th>Rate_Bachelors</th>\n",
       "      <th>Rate_Masters</th>\n",
       "      <th>Rate_Doctoral</th>\n",
       "      <th>Rate_Men</th>\n",
       "      <th>Rate_Women</th>\n",
       "      <th>Rate_Asian</th>\n",
       "      <th>Rate_Black</th>\n",
       "      <th>Rate_White</th>\n",
       "      <th>Rate_Latino</th>\n",
       "      <th>Rate_Overall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</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>2021-08-01</th>\n",
       "      <td>5.9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>4.2</td>\n",
       "      <td>6.1</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01</th>\n",
       "      <td>5.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3.9</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-01</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-01</th>\n",
       "      <td>4.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rate_High_School  Rate_Bachelors  Rate_Masters  Rate_Doctoral  \\\n",
       "DATE                                                                        \n",
       "2021-08-01               5.9             2.7           2.5            1.7   \n",
       "2021-09-01               5.7             2.5           2.1            1.6   \n",
       "2021-10-01               5.4             2.4           1.8            1.2   \n",
       "2021-11-01               5.2             2.2           1.5            0.9   \n",
       "2021-12-01               4.6             2.1           1.2            1.1   \n",
       "\n",
       "            Rate_Men  Rate_Women  Rate_Asian  Rate_Black  Rate_White  \\\n",
       "DATE                                                                   \n",
       "2021-08-01       5.3         5.0         4.6         8.7         4.5   \n",
       "2021-09-01       5.0         4.5         4.1         7.8         4.2   \n",
       "2021-10-01       4.5         4.6         4.1         7.8         3.9   \n",
       "2021-11-01       4.2         4.2         3.7         6.5         3.7   \n",
       "2021-12-01       3.9         3.9         3.6         7.1         3.2   \n",
       "\n",
       "            Rate_Latino  Rate_Overall  \n",
       "DATE                                   \n",
       "2021-08-01          6.2           5.2  \n",
       "2021-09-01          6.1           4.7  \n",
       "2021-10-01          5.7           4.6  \n",
       "2021-11-01          5.2           4.2  \n",
       "2021-12-01          4.9           3.9  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2=ARIMA(df['Rate_Overall'], order=(0,1,0))\n",
    "model2=model2.fit()\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd46b100",
   "metadata": {},
   "source": [
    "### For Future Dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9add1959",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-01    3.9\n",
      "2021-12-02    3.9\n",
      "2021-12-03    3.9\n",
      "2021-12-04    3.9\n",
      "2021-12-05    3.9\n",
      "2021-12-06    3.9\n",
      "2021-12-07    3.9\n",
      "2021-12-08    3.9\n",
      "2021-12-09    3.9\n",
      "2021-12-10    3.9\n",
      "2021-12-11    3.9\n",
      "2021-12-12    3.9\n",
      "2021-12-13    3.9\n",
      "2021-12-14    3.9\n",
      "2021-12-15    3.9\n",
      "2021-12-16    3.9\n",
      "2021-12-17    3.9\n",
      "2021-12-18    3.9\n",
      "2021-12-19    3.9\n",
      "2021-12-20    3.9\n",
      "2021-12-21    3.9\n",
      "2021-12-22    3.9\n",
      "2021-12-23    3.9\n",
      "2021-12-24    3.9\n",
      "2021-12-25    3.9\n",
      "2021-12-26    3.9\n",
      "2021-12-27    3.9\n",
      "2021-12-28    3.9\n",
      "2021-12-29    3.9\n",
      "2021-12-30    3.9\n",
      "2021-12-31    3.9\n",
      "2022-01-01    3.9\n",
      "Freq: D, Name: ARIMA Predictions, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "index_future_dates=pd.date_range(start='2021-12-01', end='2022-01-01')\n",
    "#print(index_future_dates)\n",
    "pred=model2.predict(start=len(df), end=len(df)+31, typ='levels').rename('ARIMA Predictions')\n",
    "#print(comp_pred)\n",
    "pred.index=index_future_dates\n",
    "print(pred)"
   ]
  }
 ],
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