sarima-model-AQI-forecasting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "#Importing the required libraries\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "# Ignore harmless warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from IPython.display import HTML,display\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read the daily city-wise data\n",
    "df= pd.read_csv(\"./air-quality-dataset/city_day.csv\",parse_dates=True)\n",
    "df['Date'] = pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Keeping only the columns needed\n",
    "df=df[['City','Date','AQI']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filtering the dataframe to keep the data for the city of Delhi only\n",
    "delhi = df[df['City'] == 'Delhi']\n",
    "delhi.drop(['City'],axis=1,inplace = True)\n",
    "delhi.set_index('Date', inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>AQI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>454.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>319.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-27</th>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-28</th>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-29</th>\n",
       "      <td>233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2009 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              AQI\n",
       "Date             \n",
       "2015-01-01  472.0\n",
       "2015-01-02  454.0\n",
       "2015-01-03  143.0\n",
       "2015-01-04  319.0\n",
       "2015-01-05  325.0\n",
       "...           ...\n",
       "2020-06-27  112.0\n",
       "2020-06-28  196.0\n",
       "2020-06-29  233.0\n",
       "2020-06-30  114.0\n",
       "2020-07-01  101.0\n",
       "\n",
       "[2009 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delhi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fill NULL DATA\n",
    "delhi['AQI'].fillna(method = 'bfill',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking monthly average\n",
    "delhi=delhi.astype('float64')\n",
    "delhi=delhi.resample(rule='MS').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=delhi[['AQI']].plot(figsize=(12,8),grid=True,lw=2,color='Red')\n",
    "ax.autoscale(enable=True, axis='both', tight=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FORECASTING\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delhi_AQI=delhi['AQI']\n",
    "result=seasonal_decompose(delhi_AQI,model='multiplicative')\n",
    "result.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **SARIMAX MODEL**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the SARIMAX Model\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SARIMAX stands for Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors. It is a time series forecasting model that is an extension of the ARIMA model, where the \"S\" indicates the inclusion of seasonal components, and the \"X\" indicates the inclusion of exogenous variables. SARIMAX models are commonly used to forecast time series data that exhibit seasonal patterns and may be influenced by external factors or predictors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(delhi_AQI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the training and testing dataset\n",
    "train=delhi_AQI[:48]\n",
    "test=delhi_AQI[48:61]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\DEVA\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                  <td>AQI</td>               <th>  No. Observations:  </th>    <td>48</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(0, 1, 3)x(0, 1, [1], 12)</td> <th>  Log Likelihood     </th> <td>-185.444</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                    <td>Fri, 17 Feb 2023</td>         <th>  AIC                </th>  <td>380.888</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                        <td>12:40:53</td>             <th>  BIC                </th>  <td>388.665</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                     <td>01-01-2015</td>            <th>  HQIC               </th>  <td>383.573</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td>- 12-01-2018</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>ma.L1</th>    <td>   -0.5591</td> <td>    8.218</td> <td>   -0.068</td> <td> 0.946</td> <td>  -16.666</td> <td>   15.547</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>    <td>   -0.0813</td> <td>    3.649</td> <td>   -0.022</td> <td> 0.982</td> <td>   -7.233</td> <td>    7.070</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L3</th>    <td>   -0.3558</td> <td>    3.085</td> <td>   -0.115</td> <td> 0.908</td> <td>   -6.402</td> <td>    5.690</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th> <td>   -0.9978</td> <td>  122.947</td> <td>   -0.008</td> <td> 0.994</td> <td> -241.970</td> <td>  239.975</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td> 1325.8329</td> <td> 1.62e+05</td> <td>    0.008</td> <td> 0.993</td> <td>-3.17e+05</td> <td> 3.19e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.63</td> <th>  Jarque-Bera (JB):  </th> <td>0.24</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.43</td> <th>  Prob(JB):          </th> <td>0.89</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.54</td> <th>  Skew:              </th> <td>0.20</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.29</td> <th>  Kurtosis:          </th> <td>3.01</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:                                  AQI   No. Observations:                   48\n",
       "Model:             SARIMAX(0, 1, 3)x(0, 1, [1], 12)   Log Likelihood                -185.444\n",
       "Date:                              Fri, 17 Feb 2023   AIC                            380.888\n",
       "Time:                                      12:40:53   BIC                            388.665\n",
       "Sample:                                  01-01-2015   HQIC                           383.573\n",
       "                                       - 12-01-2018                                         \n",
       "Covariance Type:                                opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ma.L1         -0.5591      8.218     -0.068      0.946     -16.666      15.547\n",
       "ma.L2         -0.0813      3.649     -0.022      0.982      -7.233       7.070\n",
       "ma.L3         -0.3558      3.085     -0.115      0.908      -6.402       5.690\n",
       "ma.S.L12      -0.9978    122.947     -0.008      0.994    -241.970     239.975\n",
       "sigma2      1325.8329   1.62e+05      0.008      0.993   -3.17e+05    3.19e+05\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.63   Jarque-Bera (JB):                 0.24\n",
       "Prob(Q):                              0.43   Prob(JB):                         0.89\n",
       "Heteroskedasticity (H):               0.54   Skew:                             0.20\n",
       "Prob(H) (two-sided):                  0.29   Kurtosis:                         3.01\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Defining our model and fitting on the training data\n",
    "model=SARIMAX(train,order=(0,1,3),seasonal_order=(0,1,1,12))\n",
    "results=model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting on our train and test dataset\n",
    "train_predictions = results.predict(start=1, end=48, typ='levels').rename('Predictions')\n",
    "test_predictions = results.predict(start=48, end=60, typ='levels').rename('Predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Date'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the train predictions to see the fit of the model\n",
    "train_predictions.plot(legend=True)\n",
    "train.plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE =  66.39476511295446\n",
      "Mean AQI 275.1711046334552\n"
     ]
    }
   ],
   "source": [
    "#Calculating the RMSE value on the training dataset \n",
    "RMSE=np.sqrt(mean_squared_error(train_predictions,train))\n",
    "print('RMSE = ',RMSE)\n",
    "print('Mean AQI',train.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Date'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the test predictions to see how well our model generalized\n",
    "test_predictions.plot(legend=True)\n",
    "test.plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE =  26.8770462682321\n",
      "Mean AQI 238.0504608294931\n"
     ]
    }
   ],
   "source": [
    "#Calculating the RMSE value on the testing dataset \n",
    "RMSE=np.sqrt(mean_squared_error(test_predictions,test))\n",
    "print('RMSE = ',RMSE)\n",
    "print('Mean AQI',test.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Date'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Obtaining predicted values:\n",
    "future_predictions = results.predict(start=67, end=77, typ='levels').rename('Predictions')\n",
    "\n",
    "#Plotting predicted values against the true values:\n",
    "future_predictions.plot(legend=True)\n",
    "delhi_AQI.plot(legend=True,figsize=(12,8),grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GRID search framework for fine tuning the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grid_search(p,d,q,P,D,Q,m,train,test):\n",
    "    \n",
    "    column_names = ['p','d','q','P','D','Q','m','RMSE_train','RMSE_test']\n",
    "    df = pd.DataFrame(columns = column_names)\n",
    "\n",
    "    for p1 in p:\n",
    "        for p2 in d:\n",
    "            for p3 in q:\n",
    "                for p4 in P:\n",
    "                    for p5 in D:\n",
    "                        for p6 in Q:\n",
    "                            for p7 in m:\n",
    "                                \n",
    "                                \n",
    "                                model=SARIMAX(train,order=(p1,p2,p3),seasonal_order=(p4,p5,p6,p7))\n",
    "                                try:\n",
    "                                    results=model.fit()\n",
    "                                except:\n",
    "                                    print(\"Passing on : \" + str(p1) + \" \" + str(p2) + \" \" + str(p3) + \" \" + str(p4) + \" \" + str(p5) + \" \" + str(p6) + \" \" + str(p7))\n",
    "                                    pass\n",
    "                                print(\"Fitting on : \" + str(p1) + \" \" + str(p2) + \" \" + str(p3) + \" \" + str(p4) + \" \" + str(p5) + \" \" + str(p6) + \" \" + str(p7))\n",
    "                                #results.summary()\n",
    "                                #Predicting on our train and test dataset\n",
    "                                train_predictions = results.predict(start=1, end=48, typ='levels').rename('Predictions')\n",
    "                                test_predictions = results.predict(start=48, end=60, typ='levels').rename('Predictions')\n",
    "                                \n",
    "                                #Calculating the RMSE value on the training dataset \n",
    "                                RMSE_train = np.sqrt(mean_squared_error(train_predictions,train))\n",
    "                                #Calculating the RMSE value on the testing dataset \n",
    "                                RMSE_test=np.sqrt(mean_squared_error(test_predictions,test))\n",
    "                                \n",
    "                                #Fill our dataframe\n",
    "                                df = df.append({'p':p1, 'd':p2,'q':p3,\n",
    "                                                'P':p4,'D':p5,'Q':p6,'m':p7,\n",
    "                                                'RMSE_train' : RMSE_train, 'RMSE_test' : RMSE_test},\n",
    "                                               ignore_index = True)\n",
    "                                print(df.tail(1))\n",
    "                                \n",
    "                                \n",
    "                                \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting on : 1 1 1 1 1 1 12\n",
      "     p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "0  1.0  1.0  1.0  1.0  1.0  1.0  12.0   62.975715  37.275076\n",
      "Fitting on : 1 1 1 1 1 2 12\n",
      "     p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "1  1.0  1.0  1.0  1.0  1.0  2.0  12.0   63.135875  46.548277\n",
      "Fitting on : 1 1 1 1 1 3 12\n",
      "     p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "2  1.0  1.0  1.0  1.0  1.0  3.0  12.0   62.958166  44.394289\n",
      "Fitting on : 1 1 1 1 1 0 12\n",
      "     p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "3  1.0  1.0  1.0  1.0  1.0  0.0  12.0    66.41462  44.756889\n",
      "Fitting on : 1 1 1 1 2 1 12\n",
      "     p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "4  1.0  1.0  1.0  1.0  2.0  1.0  12.0   113.46711  66.679343\n",
      "Fitting on : 1 1 1 1 2 2 12\n",
      "     p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "5  1.0  1.0  1.0  1.0  2.0  2.0  12.0  113.541836  58.718145\n",
      "Fitting on : 1 1 1 1 2 3 12\n",
      "     p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "6  1.0  1.0  1.0  1.0  2.0  3.0  12.0  112.872455  53.460394\n",
      "Fitting on : 1 1 1 1 2 0 12\n",
      "     p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "7  1.0  1.0  1.0  1.0  2.0  0.0  12.0  113.694741  62.133941\n",
      "Fitting on : 1 1 1 1 3 1 12\n",
      "     p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "8  1.0  1.0  1.0  1.0  3.0  1.0  12.0  149.876897  289.352387\n",
      "Fitting on : 1 1 1 1 3 2 12\n",
      "     p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "9  1.0  1.0  1.0  1.0  3.0  2.0  12.0  149.412794  291.893262\n",
      "Fitting on : 1 1 1 1 3 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "10  1.0  1.0  1.0  1.0  3.0  3.0  12.0  149.876998  286.176982\n",
      "Fitting on : 1 1 1 1 3 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "11  1.0  1.0  1.0  1.0  3.0  0.0  12.0  149.388112  281.223019\n",
      "Fitting on : 1 1 1 1 0 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "12  1.0  1.0  1.0  1.0  0.0  1.0  12.0   49.862898  110.658699\n",
      "Fitting on : 1 1 1 1 0 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "13  1.0  1.0  1.0  1.0  0.0  2.0  12.0   48.848329  89.356976\n",
      "Fitting on : 1 1 1 1 0 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "14  1.0  1.0  1.0  1.0  0.0  3.0  12.0   38.287958  112.214451\n",
      "Fitting on : 1 1 1 1 0 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "15  1.0  1.0  1.0  1.0  0.0  0.0  12.0   53.899723  44.819756\n",
      "Fitting on : 1 1 1 2 1 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "16  1.0  1.0  1.0  2.0  1.0  1.0  12.0   62.123143  50.970281\n",
      "Fitting on : 1 1 1 2 1 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "17  1.0  1.0  1.0  2.0  1.0  2.0  12.0   62.328936  59.595357\n",
      "Fitting on : 1 1 1 2 1 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "18  1.0  1.0  1.0  2.0  1.0  3.0  12.0   61.821561  43.031189\n",
      "Fitting on : 1 1 1 2 1 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "19  1.0  1.0  1.0  2.0  1.0  0.0  12.0   62.159786  41.662885\n",
      "Fitting on : 1 1 1 2 2 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "20  1.0  1.0  1.0  2.0  2.0  1.0  12.0  113.498044  90.036949\n",
      "Fitting on : 1 1 1 2 2 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "21  1.0  1.0  1.0  2.0  2.0  2.0  12.0  113.460787  89.958183\n",
      "Fitting on : 1 1 1 2 2 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "22  1.0  1.0  1.0  2.0  2.0  3.0  12.0  113.425452  82.090592\n",
      "Fitting on : 1 1 1 2 2 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "23  1.0  1.0  1.0  2.0  2.0  0.0  12.0  113.467995  91.507302\n",
      "Fitting on : 1 1 1 2 3 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "24  1.0  1.0  1.0  2.0  3.0  1.0  12.0  157.393896  528.719714\n",
      "Fitting on : 1 1 1 2 3 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "25  1.0  1.0  1.0  2.0  3.0  2.0  12.0  149.320542  286.793799\n",
      "Fitting on : 1 1 1 2 3 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "26  1.0  1.0  1.0  2.0  3.0  3.0  12.0  151.737613  334.72378\n",
      "Fitting on : 1 1 1 2 3 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "27  1.0  1.0  1.0  2.0  3.0  0.0  12.0   150.94584  327.053793\n",
      "Fitting on : 1 1 1 2 0 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "28  1.0  1.0  1.0  2.0  0.0  1.0  12.0   49.311186  85.047459\n",
      "Fitting on : 1 1 1 2 0 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "29  1.0  1.0  1.0  2.0  0.0  2.0  12.0   48.603615  92.418115\n",
      "Fitting on : 1 1 1 2 0 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "30  1.0  1.0  1.0  2.0  0.0  3.0  12.0   48.968683  88.456614\n",
      "Fitting on : 1 1 1 2 0 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "31  1.0  1.0  1.0  2.0  0.0  0.0  12.0   50.940542  67.834733\n",
      "Fitting on : 1 1 1 3 1 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "32  1.0  1.0  1.0  3.0  1.0  1.0  12.0   62.699896  70.045448\n",
      "Fitting on : 1 1 1 3 1 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "33  1.0  1.0  1.0  3.0  1.0  2.0  12.0   62.629977  54.664202\n",
      "Fitting on : 1 1 1 3 1 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "34  1.0  1.0  1.0  3.0  1.0  3.0  12.0   62.307456  57.940783\n",
      "Passing on : 1 1 1 3 1 0 12\n",
      "Fitting on : 1 1 1 3 1 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "35  1.0  1.0  1.0  3.0  1.0  0.0  12.0   62.307456  57.940783\n",
      "Fitting on : 1 1 1 3 2 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "36  1.0  1.0  1.0  3.0  2.0  1.0  12.0  113.451498  91.205431\n",
      "Fitting on : 1 1 1 3 2 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "37  1.0  1.0  1.0  3.0  2.0  2.0  12.0  113.441372  88.258545\n",
      "Fitting on : 1 1 1 3 2 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "38  1.0  1.0  1.0  3.0  2.0  3.0  12.0  113.449997  90.252887\n",
      "Fitting on : 1 1 1 3 2 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "39  1.0  1.0  1.0  3.0  2.0  0.0  12.0  113.454213  90.358763\n",
      "Fitting on : 1 1 1 3 3 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "40  1.0  1.0  1.0  3.0  3.0  1.0  12.0  292.310975  253.392258\n",
      "Fitting on : 1 1 1 3 3 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "41  1.0  1.0  1.0  3.0  3.0  2.0  12.0  151.035266  330.863335\n",
      "Fitting on : 1 1 1 3 3 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "42  1.0  1.0  1.0  3.0  3.0  3.0  12.0  149.672229  284.319856\n",
      "Fitting on : 1 1 1 3 3 0 12\n",
      "      p    d    q    P    D    Q     m    RMSE_train     RMSE_test\n",
      "43  1.0  1.0  1.0  3.0  3.0  0.0  12.0  65260.428434  191564.35036\n",
      "Fitting on : 1 1 1 3 0 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "44  1.0  1.0  1.0  3.0  0.0  1.0  12.0   48.856035  81.652896\n",
      "Fitting on : 1 1 1 3 0 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "45  1.0  1.0  1.0  3.0  0.0  2.0  12.0   49.982856  85.165373\n",
      "Fitting on : 1 1 1 3 0 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "46  1.0  1.0  1.0  3.0  0.0  3.0  12.0   47.646444  114.744615\n",
      "Fitting on : 1 1 1 3 0 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "47  1.0  1.0  1.0  3.0  0.0  0.0  12.0   48.889626  77.067317\n",
      "Fitting on : 1 1 1 0 1 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "48  1.0  1.0  1.0  0.0  1.0  1.0  12.0   64.777987   29.02666\n",
      "Fitting on : 1 1 1 0 1 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "49  1.0  1.0  1.0  0.0  1.0  2.0  12.0   63.019641  41.860041\n",
      "Fitting on : 1 1 1 0 1 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "50  1.0  1.0  1.0  0.0  1.0  3.0  12.0   62.843868  45.726389\n",
      "Fitting on : 1 1 1 0 1 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "51  1.0  1.0  1.0  0.0  1.0  0.0  12.0   73.876238  29.187665\n",
      "Fitting on : 1 1 1 0 2 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "52  1.0  1.0  1.0  0.0  2.0  1.0  12.0  112.272077  35.976658\n",
      "Fitting on : 1 1 1 0 2 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "53  1.0  1.0  1.0  0.0  2.0  2.0  12.0  112.852916  53.644383\n",
      "Fitting on : 1 1 1 0 2 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "54  1.0  1.0  1.0  0.0  2.0  3.0  12.0  113.389496  73.320437\n",
      "Fitting on : 1 1 1 0 2 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "55  1.0  1.0  1.0  0.0  2.0  0.0  12.0  116.188445  125.740729\n",
      "Fitting on : 1 1 1 0 3 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "56  1.0  1.0  1.0  0.0  3.0  1.0  12.0  149.071069  275.198094\n",
      "Fitting on : 1 1 1 0 3 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "57  1.0  1.0  1.0  0.0  3.0  2.0  12.0  149.940409  290.019496\n",
      "Fitting on : 1 1 1 0 3 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "58  1.0  1.0  1.0  0.0  3.0  3.0  12.0  150.589974  304.248193\n",
      "Fitting on : 1 1 1 0 3 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "59  1.0  1.0  1.0  0.0  3.0  0.0  12.0  149.873755  289.258964\n",
      "Fitting on : 1 1 1 0 0 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "60  1.0  1.0  1.0  0.0  0.0  1.0  12.0   32.421441  185.681894\n",
      "Fitting on : 1 1 1 0 0 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "61  1.0  1.0  1.0  0.0  0.0  2.0  12.0    38.26825  112.09769\n",
      "Fitting on : 1 1 1 0 0 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "62  1.0  1.0  1.0  0.0  0.0  3.0  12.0   41.400145  96.437664\n",
      "Fitting on : 1 1 1 0 0 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "63  1.0  1.0  1.0  0.0  0.0  0.0  12.0   28.137817  218.371063\n",
      "Fitting on : 1 1 2 1 1 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "64  1.0  1.0  2.0  1.0  1.0  1.0  12.0   64.413778  38.853087\n",
      "Fitting on : 1 1 2 1 1 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "65  1.0  1.0  2.0  1.0  1.0  2.0  12.0   64.805954  51.083106\n",
      "Fitting on : 1 1 2 1 1 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "66  1.0  1.0  2.0  1.0  1.0  3.0  12.0   64.791054  51.463569\n",
      "Fitting on : 1 1 2 1 1 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "67  1.0  1.0  2.0  1.0  1.0  0.0  12.0    67.42618  46.112137\n",
      "Fitting on : 1 1 2 1 2 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "68  1.0  1.0  2.0  1.0  2.0  1.0  12.0   111.88157   60.71698\n",
      "Fitting on : 1 1 2 1 2 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "69  1.0  1.0  2.0  1.0  2.0  2.0  12.0  113.258788  65.485976\n",
      "Fitting on : 1 1 2 1 2 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "70  1.0  1.0  2.0  1.0  2.0  3.0  12.0  112.302162  52.936007\n",
      "Fitting on : 1 1 2 1 2 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "71  1.0  1.0  2.0  1.0  2.0  0.0  12.0   112.96309  61.790121\n",
      "Fitting on : 1 1 2 1 3 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "72  1.0  1.0  2.0  1.0  3.0  1.0  12.0  149.529258  281.681609\n",
      "Fitting on : 1 1 2 1 3 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "73  1.0  1.0  2.0  1.0  3.0  2.0  12.0  149.484335  280.109217\n",
      "Fitting on : 1 1 2 1 3 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "74  1.0  1.0  2.0  1.0  3.0  3.0  12.0  150.011257  286.720291\n",
      "Fitting on : 1 1 2 1 3 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "75  1.0  1.0  2.0  1.0  3.0  0.0  12.0  149.600995  285.245638\n",
      "Fitting on : 1 1 2 1 0 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "76  1.0  1.0  2.0  1.0  0.0  1.0  12.0   54.323402  56.187305\n",
      "Fitting on : 1 1 2 1 0 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "77  1.0  1.0  2.0  1.0  0.0  2.0  12.0   53.576099  58.813012\n",
      "Fitting on : 1 1 2 1 0 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "78  1.0  1.0  2.0  1.0  0.0  3.0  12.0    47.18861  54.711295\n",
      "Fitting on : 1 1 2 1 0 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "79  1.0  1.0  2.0  1.0  0.0  0.0  12.0    53.60541  44.942206\n",
      "Fitting on : 1 1 2 2 1 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "80  1.0  1.0  2.0  2.0  1.0  1.0  12.0   63.527154  52.888988\n",
      "Passing on : 1 1 2 2 1 2 12\n",
      "Fitting on : 1 1 2 2 1 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "81  1.0  1.0  2.0  2.0  1.0  2.0  12.0   63.527154  52.888988\n",
      "Fitting on : 1 1 2 2 1 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "82  1.0  1.0  2.0  2.0  1.0  3.0  12.0   64.486977  29.993701\n",
      "Fitting on : 1 1 2 2 1 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "83  1.0  1.0  2.0  2.0  1.0  0.0  12.0   63.545486  42.936262\n",
      "Fitting on : 1 1 2 2 2 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "84  1.0  1.0  2.0  2.0  2.0  1.0  12.0  111.808858   91.76097\n",
      "Fitting on : 1 1 2 2 2 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "85  1.0  1.0  2.0  2.0  2.0  2.0  12.0  111.832815  90.714052\n",
      "Fitting on : 1 1 2 2 2 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "86  1.0  1.0  2.0  2.0  2.0  3.0  12.0  112.823273  61.802408\n",
      "Fitting on : 1 1 2 2 2 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "87  1.0  1.0  2.0  2.0  2.0  0.0  12.0  111.839393  87.634711\n",
      "Fitting on : 1 1 2 2 3 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "88  1.0  1.0  2.0  2.0  3.0  1.0  12.0  150.884471  312.528975\n",
      "Fitting on : 1 1 2 2 3 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "89  1.0  1.0  2.0  2.0  3.0  2.0  12.0  151.136525  320.169363\n",
      "Fitting on : 1 1 2 2 3 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "90  1.0  1.0  2.0  2.0  3.0  3.0  12.0  151.372448  326.560886\n",
      "Fitting on : 1 1 2 2 3 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "91  1.0  1.0  2.0  2.0  3.0  0.0  12.0  151.313548  328.250765\n",
      "Fitting on : 1 1 2 2 0 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "92  1.0  1.0  2.0  2.0  0.0  1.0  12.0   53.693903  64.448554\n",
      "Passing on : 1 1 2 2 0 2 12\n",
      "Fitting on : 1 1 2 2 0 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "93  1.0  1.0  2.0  2.0  0.0  2.0  12.0   53.693903  64.448554\n",
      "Fitting on : 1 1 2 2 0 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "94  1.0  1.0  2.0  2.0  0.0  3.0  12.0   48.716324  88.295542\n",
      "Fitting on : 1 1 2 2 0 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "95  1.0  1.0  2.0  2.0  0.0  0.0  12.0   55.758556  50.816085\n",
      "Fitting on : 1 1 2 3 1 1 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "96  1.0  1.0  2.0  3.0  1.0  1.0  12.0   64.198906  76.421774\n",
      "Fitting on : 1 1 2 3 1 2 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "97  1.0  1.0  2.0  3.0  1.0  2.0  12.0   64.214011  75.092972\n",
      "Passing on : 1 1 2 3 1 3 12\n",
      "Fitting on : 1 1 2 3 1 3 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "98  1.0  1.0  2.0  3.0  1.0  3.0  12.0   64.214011  75.092972\n",
      "Fitting on : 1 1 2 3 1 0 12\n",
      "      p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "99  1.0  1.0  2.0  3.0  1.0  0.0  12.0   64.217331  76.945978\n",
      "Fitting on : 1 1 2 3 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "100  1.0  1.0  2.0  3.0  2.0  1.0  12.0  111.867305  92.701789\n",
      "Fitting on : 1 1 2 3 2 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "101  1.0  1.0  2.0  3.0  2.0  2.0  12.0  111.924459  87.021186\n",
      "Fitting on : 1 1 2 3 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "102  1.0  1.0  2.0  3.0  2.0  3.0  12.0   111.97277  80.742123\n",
      "Fitting on : 1 1 2 3 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "103  1.0  1.0  2.0  3.0  2.0  0.0  12.0   111.85795  89.663213\n",
      "Fitting on : 1 1 2 3 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "104  1.0  1.0  2.0  3.0  3.0  1.0  12.0   151.17497  320.545145\n",
      "Fitting on : 1 1 2 3 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "105  1.0  1.0  2.0  3.0  3.0  2.0  12.0  151.301895  324.431044\n",
      "Fitting on : 1 1 2 3 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "106  1.0  1.0  2.0  3.0  3.0  3.0  12.0  151.172384  320.937654\n",
      "Fitting on : 1 1 2 3 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "107  1.0  1.0  2.0  3.0  3.0  0.0  12.0   152.16358  355.372242\n",
      "Passing on : 1 1 2 3 0 1 12\n",
      "Fitting on : 1 1 2 3 0 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "108  1.0  1.0  2.0  3.0  0.0  1.0  12.0   152.16358  355.372242\n",
      "Fitting on : 1 1 2 3 0 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "109  1.0  1.0  2.0  3.0  0.0  2.0  12.0   53.645927  68.640833\n",
      "Fitting on : 1 1 2 3 0 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "110  1.0  1.0  2.0  3.0  0.0  3.0  12.0   47.960604  77.208855\n",
      "Fitting on : 1 1 2 3 0 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "111  1.0  1.0  2.0  3.0  0.0  0.0  12.0    51.15911  73.618953\n",
      "Fitting on : 1 1 2 0 1 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "112  1.0  1.0  2.0  0.0  1.0  1.0  12.0   64.533447  29.335584\n",
      "Fitting on : 1 1 2 0 1 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "113  1.0  1.0  2.0  0.0  1.0  2.0  12.0   64.100635  42.186709\n",
      "Fitting on : 1 1 2 0 1 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "114  1.0  1.0  2.0  0.0  1.0  3.0  12.0   64.709467  51.080714\n",
      "Fitting on : 1 1 2 0 1 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "115  1.0  1.0  2.0  0.0  1.0  0.0  12.0   73.778699  29.463656\n",
      "Fitting on : 1 1 2 0 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "116  1.0  1.0  2.0  0.0  2.0  1.0  12.0  112.616021  35.579213\n",
      "Fitting on : 1 1 2 0 2 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "117  1.0  1.0  2.0  0.0  2.0  2.0  12.0  112.320628  53.007605\n",
      "Fitting on : 1 1 2 0 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "118  1.0  1.0  2.0  0.0  2.0  3.0  12.0  112.121728  70.886767\n",
      "Fitting on : 1 1 2 0 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "119  1.0  1.0  2.0  0.0  2.0  0.0  12.0  116.256176  107.13655\n",
      "Fitting on : 1 1 2 0 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "120  1.0  1.0  2.0  0.0  3.0  1.0  12.0  149.612634  282.638295\n",
      "Fitting on : 1 1 2 0 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "121  1.0  1.0  2.0  0.0  3.0  2.0  12.0   150.44805  295.818389\n",
      "Fitting on : 1 1 2 0 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "122  1.0  1.0  2.0  0.0  3.0  3.0  12.0  149.882562  284.07551\n",
      "Fitting on : 1 1 2 0 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "123  1.0  1.0  2.0  0.0  3.0  0.0  12.0  150.433997  297.662696\n",
      "Fitting on : 1 1 2 0 0 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "124  1.0  1.0  2.0  0.0  0.0  1.0  12.0   39.695264  75.305847\n",
      "Fitting on : 1 1 2 0 0 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "125  1.0  1.0  2.0  0.0  0.0  2.0  12.0   47.158139  54.681146\n",
      "Fitting on : 1 1 2 0 0 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "126  1.0  1.0  2.0  0.0  0.0  3.0  12.0   49.481378  47.522086\n",
      "Fitting on : 1 1 2 0 0 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "127  1.0  1.0  2.0  0.0  0.0  0.0  12.0   34.050067  99.565327\n",
      "Fitting on : 1 1 3 1 1 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "128  1.0  1.0  3.0  1.0  1.0  1.0  12.0   64.291825  38.385972\n",
      "Fitting on : 1 1 3 1 1 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "129  1.0  1.0  3.0  1.0  1.0  2.0  12.0   62.117189  44.285805\n",
      "Fitting on : 1 1 3 1 1 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "130  1.0  1.0  3.0  1.0  1.0  3.0  12.0   66.325873  44.222842\n",
      "Fitting on : 1 1 3 1 1 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "131  1.0  1.0  3.0  1.0  1.0  0.0  12.0   67.654159  46.573701\n",
      "Fitting on : 1 1 3 1 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "132  1.0  1.0  3.0  1.0  2.0  1.0  12.0  112.691916   68.16174\n",
      "Fitting on : 1 1 3 1 2 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "133  1.0  1.0  3.0  1.0  2.0  2.0  12.0  113.401327  60.776349\n",
      "Fitting on : 1 1 3 1 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "134  1.0  1.0  3.0  1.0  2.0  3.0  12.0  113.036326   53.35607\n",
      "Fitting on : 1 1 3 1 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "135  1.0  1.0  3.0  1.0  2.0  0.0  12.0  114.301728  62.444853\n",
      "Passing on : 1 1 3 1 3 1 12\n",
      "Fitting on : 1 1 3 1 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "136  1.0  1.0  3.0  1.0  3.0  1.0  12.0  114.301728  62.444853\n",
      "Passing on : 1 1 3 1 3 2 12\n",
      "Fitting on : 1 1 3 1 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "137  1.0  1.0  3.0  1.0  3.0  2.0  12.0  114.301728  62.444853\n",
      "Passing on : 1 1 3 1 3 3 12\n",
      "Fitting on : 1 1 3 1 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "138  1.0  1.0  3.0  1.0  3.0  3.0  12.0  114.301728  62.444853\n",
      "Passing on : 1 1 3 1 3 0 12\n",
      "Fitting on : 1 1 3 1 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "139  1.0  1.0  3.0  1.0  3.0  0.0  12.0  114.301728  62.444853\n",
      "Fitting on : 1 1 3 1 0 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "140  1.0  1.0  3.0  1.0  0.0  1.0  12.0   54.295257  56.528966\n",
      "Fitting on : 1 1 3 1 0 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "141  1.0  1.0  3.0  1.0  0.0  2.0  12.0   53.203089  65.416459\n",
      "Fitting on : 1 1 3 1 0 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "142  1.0  1.0  3.0  1.0  0.0  3.0  12.0   46.981214  54.406011\n",
      "Fitting on : 1 1 3 1 0 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "143  1.0  1.0  3.0  1.0  0.0  0.0  12.0   53.887814  44.180559\n",
      "Fitting on : 1 1 3 2 1 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "144  1.0  1.0  3.0  2.0  1.0  1.0  12.0   63.237688  50.141347\n",
      "Fitting on : 1 1 3 2 1 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "145  1.0  1.0  3.0  2.0  1.0  2.0  12.0   68.253929  59.675229\n",
      "Fitting on : 1 1 3 2 1 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "146  1.0  1.0  3.0  2.0  1.0  3.0  12.0   68.002034  63.587148\n",
      "Fitting on : 1 1 3 2 1 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "147  1.0  1.0  3.0  2.0  1.0  0.0  12.0   63.269187  43.738292\n",
      "Fitting on : 1 1 3 2 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "148  1.0  1.0  3.0  2.0  2.0  1.0  12.0  111.975773  91.804293\n",
      "Fitting on : 1 1 3 2 2 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "149  1.0  1.0  3.0  2.0  2.0  2.0  12.0  112.112067  85.240844\n",
      "Fitting on : 1 1 3 2 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "150  1.0  1.0  3.0  2.0  2.0  3.0  12.0  112.380708  74.977355\n",
      "Fitting on : 1 1 3 2 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "151  1.0  1.0  3.0  2.0  2.0  0.0  12.0  111.997685   92.18122\n",
      "Passing on : 1 1 3 2 3 1 12\n",
      "Fitting on : 1 1 3 2 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "152  1.0  1.0  3.0  2.0  3.0  1.0  12.0  111.997685   92.18122\n",
      "Passing on : 1 1 3 2 3 2 12\n",
      "Fitting on : 1 1 3 2 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "153  1.0  1.0  3.0  2.0  3.0  2.0  12.0  111.997685   92.18122\n",
      "Passing on : 1 1 3 2 3 3 12\n",
      "Fitting on : 1 1 3 2 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "154  1.0  1.0  3.0  2.0  3.0  3.0  12.0  111.997685   92.18122\n",
      "Passing on : 1 1 3 2 3 0 12\n",
      "Fitting on : 1 1 3 2 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "155  1.0  1.0  3.0  2.0  3.0  0.0  12.0  111.997685   92.18122\n",
      "Fitting on : 1 1 3 2 0 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "156  1.0  1.0  3.0  2.0  0.0  1.0  12.0   62.612086  30.821919\n",
      "Passing on : 1 1 3 2 0 2 12\n",
      "Fitting on : 1 1 3 2 0 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "157  1.0  1.0  3.0  2.0  0.0  2.0  12.0   62.612086  30.821919\n",
      "Fitting on : 1 1 3 2 0 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "158  1.0  1.0  3.0  2.0  0.0  3.0  12.0   53.428103  78.834504\n",
      "Fitting on : 1 1 3 2 0 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "159  1.0  1.0  3.0  2.0  0.0  0.0  12.0   55.749616  51.502484\n",
      "Fitting on : 1 1 3 3 1 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "160  1.0  1.0  3.0  3.0  1.0  1.0  12.0   68.445165  64.139786\n",
      "Fitting on : 1 1 3 3 1 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "161  1.0  1.0  3.0  3.0  1.0  2.0  12.0   68.373278  62.535287\n",
      "Fitting on : 1 1 3 3 1 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "162  1.0  1.0  3.0  3.0  1.0  3.0  12.0   67.541935  59.151483\n",
      "Fitting on : 1 1 3 3 1 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "163  1.0  1.0  3.0  3.0  1.0  0.0  12.0   67.911195  75.528265\n",
      "Fitting on : 1 1 3 3 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "164  1.0  1.0  3.0  3.0  2.0  1.0  12.0  111.958668  93.152022\n",
      "Fitting on : 1 1 3 3 2 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "165  1.0  1.0  3.0  3.0  2.0  2.0  12.0  112.004821  91.104683\n",
      "Fitting on : 1 1 3 3 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "166  1.0  1.0  3.0  3.0  2.0  3.0  12.0  112.261987  85.570519\n",
      "Fitting on : 1 1 3 3 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "167  1.0  1.0  3.0  3.0  2.0  0.0  12.0  111.983744  91.781609\n",
      "Passing on : 1 1 3 3 3 1 12\n",
      "Fitting on : 1 1 3 3 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "168  1.0  1.0  3.0  3.0  3.0  1.0  12.0  111.983744  91.781609\n",
      "Passing on : 1 1 3 3 3 2 12\n",
      "Fitting on : 1 1 3 3 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "169  1.0  1.0  3.0  3.0  3.0  2.0  12.0  111.983744  91.781609\n",
      "Passing on : 1 1 3 3 3 3 12\n",
      "Fitting on : 1 1 3 3 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "170  1.0  1.0  3.0  3.0  3.0  3.0  12.0  111.983744  91.781609\n",
      "Passing on : 1 1 3 3 3 0 12\n",
      "Fitting on : 1 1 3 3 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "171  1.0  1.0  3.0  3.0  3.0  0.0  12.0  111.983744  91.781609\n",
      "Fitting on : 1 1 3 3 0 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "172  1.0  1.0  3.0  3.0  0.0  1.0  12.0     62.6751  40.005729\n",
      "Fitting on : 1 1 3 3 0 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "173  1.0  1.0  3.0  3.0  0.0  2.0  12.0   53.708723  67.297695\n",
      "Fitting on : 1 1 3 3 0 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "174  1.0  1.0  3.0  3.0  0.0  3.0  12.0   52.760121  70.805133\n",
      "Fitting on : 1 1 3 3 0 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "175  1.0  1.0  3.0  3.0  0.0  0.0  12.0   64.702972  37.864096\n",
      "Fitting on : 1 1 3 0 1 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "176  1.0  1.0  3.0  0.0  1.0  1.0  12.0   64.996638  28.836871\n",
      "Fitting on : 1 1 3 0 1 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "177  1.0  1.0  3.0  0.0  1.0  2.0  12.0   63.953071  41.534914\n",
      "Fitting on : 1 1 3 0 1 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "178  1.0  1.0  3.0  0.0  1.0  3.0  12.0   64.596275  50.373034\n",
      "Fitting on : 1 1 3 0 1 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "179  1.0  1.0  3.0  0.0  1.0  0.0  12.0   74.693709  28.471944\n",
      "Fitting on : 1 1 3 0 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "180  1.0  1.0  3.0  0.0  2.0  1.0  12.0  114.438693  35.891662\n",
      "Fitting on : 1 1 3 0 2 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "181  1.0  1.0  3.0  0.0  2.0  2.0  12.0  113.052722  53.398727\n",
      "Fitting on : 1 1 3 0 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "182  1.0  1.0  3.0  0.0  2.0  3.0  12.0  112.503432  66.151977\n",
      "Fitting on : 1 1 3 0 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "183  1.0  1.0  3.0  0.0  2.0  0.0  12.0  118.350604  78.099593\n",
      "Passing on : 1 1 3 0 3 1 12\n",
      "Fitting on : 1 1 3 0 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "184  1.0  1.0  3.0  0.0  3.0  1.0  12.0  118.350604  78.099593\n",
      "Passing on : 1 1 3 0 3 2 12\n",
      "Fitting on : 1 1 3 0 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "185  1.0  1.0  3.0  0.0  3.0  2.0  12.0  118.350604  78.099593\n",
      "Passing on : 1 1 3 0 3 3 12\n",
      "Fitting on : 1 1 3 0 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "186  1.0  1.0  3.0  0.0  3.0  3.0  12.0  118.350604  78.099593\n",
      "Passing on : 1 1 3 0 3 0 12\n",
      "Fitting on : 1 1 3 0 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "187  1.0  1.0  3.0  0.0  3.0  0.0  12.0  118.350604  78.099593\n",
      "Fitting on : 1 1 3 0 0 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "188  1.0  1.0  3.0  0.0  0.0  1.0  12.0   38.371114  73.566448\n",
      "Fitting on : 1 1 3 0 0 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "189  1.0  1.0  3.0  0.0  0.0  2.0  12.0   47.043659  54.272141\n",
      "Fitting on : 1 1 3 0 0 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "190  1.0  1.0  3.0  0.0  0.0  3.0  12.0   49.469418  47.623517\n",
      "Fitting on : 1 1 3 0 0 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "191  1.0  1.0  3.0  0.0  0.0  0.0  12.0   34.180436   97.56038\n",
      "Fitting on : 1 1 0 1 1 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "192  1.0  1.0  0.0  1.0  1.0  1.0  12.0   60.611827  70.399517\n",
      "Fitting on : 1 1 0 1 1 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "193  1.0  1.0  0.0  1.0  1.0  2.0  12.0   59.649807  81.550719\n",
      "Fitting on : 1 1 0 1 1 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "194  1.0  1.0  0.0  1.0  1.0  3.0  12.0   59.649418  78.019277\n",
      "Fitting on : 1 1 0 1 1 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "195  1.0  1.0  0.0  1.0  1.0  0.0  12.0    64.69604  49.765576\n",
      "Fitting on : 1 1 0 1 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "196  1.0  1.0  0.0  1.0  2.0  1.0  12.0  113.831698  69.580037\n",
      "Fitting on : 1 1 0 1 2 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "197  1.0  1.0  0.0  1.0  2.0  2.0  12.0  114.117958  85.391242\n",
      "Fitting on : 1 1 0 1 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "198  1.0  1.0  0.0  1.0  2.0  3.0  12.0  113.059871  58.574572\n",
      "Fitting on : 1 1 0 1 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "199  1.0  1.0  0.0  1.0  2.0  0.0  12.0  113.400022  52.294058\n",
      "Fitting on : 1 1 0 1 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "200  1.0  1.0  0.0  1.0  3.0  1.0  12.0  152.986201  352.891012\n",
      "Fitting on : 1 1 0 1 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "201  1.0  1.0  0.0  1.0  3.0  2.0  12.0  154.131502  365.834374\n",
      "Fitting on : 1 1 0 1 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "202  1.0  1.0  0.0  1.0  3.0  3.0  12.0  153.623316  356.429148\n",
      "Fitting on : 1 1 0 1 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "203  1.0  1.0  0.0  1.0  3.0  0.0  12.0  152.981451  353.479221\n",
      "Fitting on : 1 1 0 1 0 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "204  1.0  1.0  0.0  1.0  0.0  1.0  12.0   47.821181  92.111715\n",
      "Fitting on : 1 1 0 1 0 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "205  1.0  1.0  0.0  1.0  0.0  2.0  12.0   47.535266  84.283354\n",
      "Fitting on : 1 1 0 1 0 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "206  1.0  1.0  0.0  1.0  0.0  3.0  12.0   47.894118  82.487193\n",
      "Fitting on : 1 1 0 1 0 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "207  1.0  1.0  0.0  1.0  0.0  0.0  12.0   44.916302  108.824582\n",
      "Fitting on : 1 1 0 2 1 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "208  1.0  1.0  0.0  2.0  1.0  1.0  12.0   59.716127  59.091015\n",
      "Fitting on : 1 1 0 2 1 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "209  1.0  1.0  0.0  2.0  1.0  2.0  12.0   59.710274  65.100994\n",
      "Fitting on : 1 1 0 2 1 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "210  1.0  1.0  0.0  2.0  1.0  3.0  12.0   59.678047  63.468765\n",
      "Fitting on : 1 1 0 2 1 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "211  1.0  1.0  0.0  2.0  1.0  0.0  12.0   59.690565  64.629406\n",
      "Fitting on : 1 1 0 2 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "212  1.0  1.0  0.0  2.0  2.0  1.0  12.0  114.422213  104.988972\n",
      "Fitting on : 1 1 0 2 2 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "213  1.0  1.0  0.0  2.0  2.0  2.0  12.0  114.320077  100.322126\n",
      "Fitting on : 1 1 0 2 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "214  1.0  1.0  0.0  2.0  2.0  3.0  12.0  114.298963  97.752967\n",
      "Fitting on : 1 1 0 2 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "215  1.0  1.0  0.0  2.0  2.0  0.0  12.0  114.528173  111.429793\n",
      "Fitting on : 1 1 0 2 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "216  1.0  1.0  0.0  2.0  3.0  1.0  12.0  152.959687  353.097076\n",
      "Fitting on : 1 1 0 2 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "217  1.0  1.0  0.0  2.0  3.0  2.0  12.0  152.850359  352.117546\n",
      "Fitting on : 1 1 0 2 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "218  1.0  1.0  0.0  2.0  3.0  3.0  12.0   154.14586  365.344111\n",
      "Fitting on : 1 1 0 2 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "219  1.0  1.0  0.0  2.0  3.0  0.0  12.0  153.044042  353.880712\n",
      "Fitting on : 1 1 0 2 0 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "220  1.0  1.0  0.0  2.0  0.0  1.0  12.0   48.251267  83.967086\n",
      "Fitting on : 1 1 0 2 0 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "221  1.0  1.0  0.0  2.0  0.0  2.0  12.0   47.898418  86.778222\n",
      "Fitting on : 1 1 0 2 0 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "222  1.0  1.0  0.0  2.0  0.0  3.0  12.0   47.898046  89.410035\n",
      "Fitting on : 1 1 0 2 0 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "223  1.0  1.0  0.0  2.0  0.0  0.0  12.0   49.886355  63.891934\n",
      "Fitting on : 1 1 0 3 1 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "224  1.0  1.0  0.0  3.0  1.0  1.0  12.0    59.61598   95.73253\n",
      "Fitting on : 1 1 0 3 1 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "225  1.0  1.0  0.0  3.0  1.0  2.0  12.0    59.65607  99.537702\n",
      "Fitting on : 1 1 0 3 1 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "226  1.0  1.0  0.0  3.0  1.0  3.0  12.0   59.672174  86.501422\n",
      "Fitting on : 1 1 0 3 1 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "227  1.0  1.0  0.0  3.0  1.0  0.0  12.0   59.622434  105.518356\n",
      "Fitting on : 1 1 0 3 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "228  1.0  1.0  0.0  3.0  2.0  1.0  12.0  114.418911  101.223894\n",
      "Fitting on : 1 1 0 3 2 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "229  1.0  1.0  0.0  3.0  2.0  2.0  12.0  114.427048  108.879785\n",
      "Fitting on : 1 1 0 3 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "230  1.0  1.0  0.0  3.0  2.0  3.0  12.0  113.990855  79.650814\n",
      "Fitting on : 1 1 0 3 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "231  1.0  1.0  0.0  3.0  2.0  0.0  12.0  114.573317  113.195808\n",
      "Fitting on : 1 1 0 3 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "232  1.0  1.0  0.0  3.0  3.0  1.0  12.0  152.913885  349.680771\n",
      "Fitting on : 1 1 0 3 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "233  1.0  1.0  0.0  3.0  3.0  2.0  12.0  152.959232  353.628543\n",
      "Fitting on : 1 1 0 3 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "234  1.0  1.0  0.0  3.0  3.0  3.0  12.0  153.122057  351.967085\n",
      "Fitting on : 1 1 0 3 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "235  1.0  1.0  0.0  3.0  3.0  0.0  12.0  152.874969  352.813574\n",
      "Fitting on : 1 1 0 3 0 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "236  1.0  1.0  0.0  3.0  0.0  1.0  12.0   47.892191  73.616062\n",
      "Passing on : 1 1 0 3 0 2 12\n",
      "Fitting on : 1 1 0 3 0 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "237  1.0  1.0  0.0  3.0  0.0  2.0  12.0   47.892191  73.616062\n",
      "Fitting on : 1 1 0 3 0 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "238  1.0  1.0  0.0  3.0  0.0  3.0  12.0     47.3338  96.924972\n",
      "Fitting on : 1 1 0 3 0 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "239  1.0  1.0  0.0  3.0  0.0  0.0  12.0   47.893832  75.653588\n",
      "Fitting on : 1 1 0 0 1 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "240  1.0  1.0  0.0  0.0  1.0  1.0  12.0   61.468591  72.673261\n",
      "Fitting on : 1 1 0 0 1 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "241  1.0  1.0  0.0  0.0  1.0  2.0  12.0   59.563057  77.799517\n",
      "Fitting on : 1 1 0 0 1 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "242  1.0  1.0  0.0  0.0  1.0  3.0  12.0    59.64727  79.574909\n",
      "Fitting on : 1 1 0 0 1 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "243  1.0  1.0  0.0  0.0  1.0  0.0  12.0   72.880348   67.72009\n",
      "Fitting on : 1 1 0 0 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "244  1.0  1.0  0.0  0.0  2.0  1.0  12.0  113.528544  34.978733\n",
      "Fitting on : 1 1 0 0 2 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "245  1.0  1.0  0.0  0.0  2.0  2.0  12.0  113.048098  58.542028\n",
      "Fitting on : 1 1 0 0 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "246  1.0  1.0  0.0  0.0  2.0  3.0  12.0  112.986292  78.662857\n",
      "Fitting on : 1 1 0 0 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "247  1.0  1.0  0.0  0.0  2.0  0.0  12.0  118.253265  162.731121\n",
      "Fitting on : 1 1 0 0 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "248  1.0  1.0  0.0  0.0  3.0  1.0  12.0  154.119203  365.678235\n",
      "Fitting on : 1 1 0 0 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "249  1.0  1.0  0.0  0.0  3.0  2.0  12.0  153.768322  359.135357\n",
      "Fitting on : 1 1 0 0 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "250  1.0  1.0  0.0  0.0  3.0  3.0  12.0  154.493168  372.28878\n",
      "Fitting on : 1 1 0 0 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "251  1.0  1.0  0.0  0.0  3.0  0.0  12.0  155.160961  386.766991\n",
      "Fitting on : 1 1 0 0 0 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "252  1.0  1.0  0.0  0.0  0.0  1.0  12.0   26.589773  168.701803\n",
      "Fitting on : 1 1 0 0 0 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "253  1.0  1.0  0.0  0.0  0.0  2.0  12.0   36.519965  102.615941\n",
      "Fitting on : 1 1 0 0 0 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "254  1.0  1.0  0.0  0.0  0.0  3.0  12.0   39.607508  85.978459\n",
      "Fitting on : 1 1 0 0 0 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "255  1.0  1.0  0.0  0.0  0.0  0.0  12.0   18.914605  193.676592\n",
      "Fitting on : 1 2 1 1 1 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "256  1.0  2.0  1.0  1.0  1.0  1.0  12.0   77.056856  72.183689\n",
      "Fitting on : 1 2 1 1 1 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "257  1.0  2.0  1.0  1.0  1.0  2.0  12.0   78.424772  64.706282\n",
      "Fitting on : 1 2 1 1 1 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "258  1.0  2.0  1.0  1.0  1.0  3.0  12.0   76.544572  85.355042\n",
      "Fitting on : 1 2 1 1 1 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "259  1.0  2.0  1.0  1.0  1.0  0.0  12.0   80.258669  47.862039\n",
      "Fitting on : 1 2 1 1 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "260  1.0  2.0  1.0  1.0  2.0  1.0  12.0  131.851819  94.549625\n",
      "Fitting on : 1 2 1 1 2 2 12\n",
      "       p    d    q    P    D    Q     m    RMSE_train      RMSE_test\n",
      "261  1.0  2.0  1.0  1.0  2.0  2.0  12.0  22706.982681  134209.225497\n",
      "Fitting on : 1 2 1 1 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "262  1.0  2.0  1.0  1.0  2.0  3.0  12.0  132.061677  108.479523\n",
      "Fitting on : 1 2 1 1 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "263  1.0  2.0  1.0  1.0  2.0  0.0  12.0  132.119431  94.735686\n",
      "Fitting on : 1 2 1 1 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "264  1.0  2.0  1.0  1.0  3.0  1.0  12.0  187.270031  424.149966\n",
      "Fitting on : 1 2 1 1 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "265  1.0  2.0  1.0  1.0  3.0  2.0  12.0  187.248281  424.213663\n",
      "Fitting on : 1 2 1 1 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "266  1.0  2.0  1.0  1.0  3.0  3.0  12.0  188.001813  441.51957\n",
      "Fitting on : 1 2 1 1 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "267  1.0  2.0  1.0  1.0  3.0  0.0  12.0  187.261617  424.245228\n",
      "Fitting on : 1 2 1 1 0 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "268  1.0  2.0  1.0  1.0  0.0  1.0  12.0   55.024812  93.036556\n",
      "Fitting on : 1 2 1 1 0 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "269  1.0  2.0  1.0  1.0  0.0  2.0  12.0    54.97969  87.016307\n",
      "Fitting on : 1 2 1 1 0 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "270  1.0  2.0  1.0  1.0  0.0  3.0  12.0   45.197402  114.832483\n",
      "Fitting on : 1 2 1 1 0 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "271  1.0  2.0  1.0  1.0  0.0  0.0  12.0   52.545995  117.311154\n",
      "Fitting on : 1 2 1 2 1 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "272  1.0  2.0  1.0  2.0  1.0  1.0  12.0   76.584361   60.07005\n",
      "Fitting on : 1 2 1 2 1 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "273  1.0  2.0  1.0  2.0  1.0  2.0  12.0   76.542916  82.133435\n",
      "Fitting on : 1 2 1 2 1 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "274  1.0  2.0  1.0  2.0  1.0  3.0  12.0   76.568634  65.823262\n",
      "Fitting on : 1 2 1 2 1 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "275  1.0  2.0  1.0  2.0  1.0  0.0  12.0   76.565219  66.496016\n",
      "Fitting on : 1 2 1 2 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "276  1.0  2.0  1.0  2.0  2.0  1.0  12.0  132.301318  102.20363\n",
      "Fitting on : 1 2 1 2 2 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "277  1.0  2.0  1.0  2.0  2.0  2.0  12.0  131.667971  104.694316\n",
      "Fitting on : 1 2 1 2 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "278  1.0  2.0  1.0  2.0  2.0  3.0  12.0  132.672171  149.431153\n",
      "Fitting on : 1 2 1 2 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "279  1.0  2.0  1.0  2.0  2.0  0.0  12.0  131.627586  107.486681\n",
      "Fitting on : 1 2 1 2 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "280  1.0  2.0  1.0  2.0  3.0  1.0  12.0  187.279364  424.232179\n",
      "Fitting on : 1 2 1 2 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "281  1.0  2.0  1.0  2.0  3.0  2.0  12.0  187.368584  424.845263\n",
      "Fitting on : 1 2 1 2 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "282  1.0  2.0  1.0  2.0  3.0  3.0  12.0  187.725133  434.95436\n",
      "Fitting on : 1 2 1 2 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "283  1.0  2.0  1.0  2.0  3.0  0.0  12.0  186.968066  424.546782\n",
      "Fitting on : 1 2 1 2 0 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "284  1.0  2.0  1.0  2.0  0.0  1.0  12.0    56.53785  89.729399\n",
      "Fitting on : 1 2 1 2 0 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "285  1.0  2.0  1.0  2.0  0.0  2.0  12.0   55.385025  81.844527\n",
      "Fitting on : 1 2 1 2 0 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "286  1.0  2.0  1.0  2.0  0.0  3.0  12.0   55.439652  92.289713\n",
      "Fitting on : 1 2 1 2 0 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "287  1.0  2.0  1.0  2.0  0.0  0.0  12.0   57.149796  65.744224\n",
      "Fitting on : 1 2 1 3 1 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "288  1.0  2.0  1.0  3.0  1.0  1.0  12.0   76.545789  68.845873\n",
      "Fitting on : 1 2 1 3 1 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "289  1.0  2.0  1.0  3.0  1.0  2.0  12.0   76.588623  60.056027\n",
      "Fitting on : 1 2 1 3 1 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "290  1.0  2.0  1.0  3.0  1.0  3.0  12.0   76.567946  75.107736\n",
      "Fitting on : 1 2 1 3 1 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "291  1.0  2.0  1.0  3.0  1.0  0.0  12.0    76.51566  112.571081\n",
      "Fitting on : 1 2 1 3 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "292  1.0  2.0  1.0  3.0  2.0  1.0  12.0  131.701899  101.239969\n",
      "Fitting on : 1 2 1 3 2 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "293  1.0  2.0  1.0  3.0  2.0  2.0  12.0  131.714294  103.132363\n",
      "Fitting on : 1 2 1 3 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "294  1.0  2.0  1.0  3.0  2.0  3.0  12.0  131.654208  105.747139\n",
      "Fitting on : 1 2 1 3 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "295  1.0  2.0  1.0  3.0  2.0  0.0  12.0  131.650259  105.553895\n",
      "Fitting on : 1 2 1 3 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "296  1.0  2.0  1.0  3.0  3.0  1.0  12.0  187.488084  425.066103\n",
      "Fitting on : 1 2 1 3 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "297  1.0  2.0  1.0  3.0  3.0  2.0  12.0  187.236303  427.327726\n",
      "Fitting on : 1 2 1 3 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "298  1.0  2.0  1.0  3.0  3.0  3.0  12.0  187.464443  425.831235\n",
      "Fitting on : 1 2 1 3 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "299  1.0  2.0  1.0  3.0  3.0  0.0  12.0  187.109953  423.123654\n",
      "Fitting on : 1 2 1 3 0 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "300  1.0  2.0  1.0  3.0  0.0  1.0  12.0   55.394655  83.644662\n",
      "Fitting on : 1 2 1 3 0 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "301  1.0  2.0  1.0  3.0  0.0  2.0  12.0   55.071769  87.416034\n",
      "Fitting on : 1 2 1 3 0 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "302  1.0  2.0  1.0  3.0  0.0  3.0  12.0   55.016898   96.72944\n",
      "Fitting on : 1 2 1 3 0 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "303  1.0  2.0  1.0  3.0  0.0  0.0  12.0   55.397308  77.633913\n",
      "Fitting on : 1 2 1 0 1 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "304  1.0  2.0  1.0  0.0  1.0  1.0  12.0   77.868481  68.230389\n",
      "Fitting on : 1 2 1 0 1 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "305  1.0  2.0  1.0  0.0  1.0  2.0  12.0   76.403372  77.836846\n",
      "Fitting on : 1 2 1 0 1 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "306  1.0  2.0  1.0  0.0  1.0  3.0  12.0    76.51507    80.4391\n",
      "Fitting on : 1 2 1 0 1 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "307  1.0  2.0  1.0  0.0  1.0  0.0  12.0   87.280566  62.018682\n",
      "Fitting on : 1 2 1 0 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "308  1.0  2.0  1.0  0.0  2.0  1.0  12.0  132.724483  149.558663\n",
      "Fitting on : 1 2 1 0 2 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "309  1.0  2.0  1.0  0.0  2.0  2.0  12.0  132.081104  107.942095\n",
      "Fitting on : 1 2 1 0 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "310  1.0  2.0  1.0  0.0  2.0  3.0  12.0  131.751184  98.812323\n",
      "Fitting on : 1 2 1 0 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "311  1.0  2.0  1.0  0.0  2.0  0.0  12.0   136.88745  254.312209\n",
      "Fitting on : 1 2 1 0 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "312  1.0  2.0  1.0  0.0  3.0  1.0  12.0  188.486117  454.632427\n",
      "Fitting on : 1 2 1 0 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "313  1.0  2.0  1.0  0.0  3.0  2.0  12.0   187.96678  442.355281\n",
      "Fitting on : 1 2 1 0 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "314  1.0  2.0  1.0  0.0  3.0  3.0  12.0  188.824481  458.487954\n",
      "Fitting on : 1 2 1 0 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "315  1.0  2.0  1.0  0.0  3.0  0.0  12.0  190.199546  472.386709\n",
      "Fitting on : 1 2 1 0 0 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "316  1.0  2.0  1.0  0.0  0.0  1.0  12.0    37.98999  185.008356\n",
      "Fitting on : 1 2 1 0 0 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "317  1.0  2.0  1.0  0.0  0.0  2.0  12.0   45.213418  114.669956\n",
      "Fitting on : 1 2 1 0 0 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "318  1.0  2.0  1.0  0.0  0.0  3.0  12.0    47.72802   96.13038\n",
      "Fitting on : 1 2 1 0 0 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "319  1.0  2.0  1.0  0.0  0.0  0.0  12.0   35.064828  203.431246\n",
      "Fitting on : 1 2 2 1 1 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "320  1.0  2.0  2.0  1.0  1.0  1.0  12.0   76.962007  83.457822\n",
      "Fitting on : 1 2 2 1 1 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "321  1.0  2.0  2.0  1.0  1.0  2.0  12.0    76.93189  83.643494\n",
      "Fitting on : 1 2 2 1 1 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "322  1.0  2.0  2.0  1.0  1.0  3.0  12.0    76.94923  87.754265\n",
      "Fitting on : 1 2 2 1 1 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "323  1.0  2.0  2.0  1.0  1.0  0.0  12.0   80.486545  51.281997\n",
      "Fitting on : 1 2 2 1 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "324  1.0  2.0  2.0  1.0  2.0  1.0  12.0   133.46274    90.1228\n",
      "Fitting on : 1 2 2 1 2 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "325  1.0  2.0  2.0  1.0  2.0  2.0  12.0  133.448122   90.24961\n",
      "Fitting on : 1 2 2 1 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "326  1.0  2.0  2.0  1.0  2.0  3.0  12.0  132.903588  103.671197\n",
      "Fitting on : 1 2 2 1 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "327  1.0  2.0  2.0  1.0  2.0  0.0  12.0  133.738665  96.579278\n",
      "Fitting on : 1 2 2 1 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "328  1.0  2.0  2.0  1.0  3.0  1.0  12.0  187.492682  437.310987\n",
      "Fitting on : 1 2 2 1 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "329  1.0  2.0  2.0  1.0  3.0  2.0  12.0  187.428495  688.442285\n",
      "Fitting on : 1 2 2 1 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "330  1.0  2.0  2.0  1.0  3.0  3.0  12.0  188.101895  445.775942\n",
      "Fitting on : 1 2 2 1 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "331  1.0  2.0  2.0  1.0  3.0  0.0  12.0  187.113628  689.163515\n",
      "Fitting on : 1 2 2 1 0 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "332  1.0  2.0  2.0  1.0  0.0  1.0  12.0   56.999654  111.720682\n",
      "Fitting on : 1 2 2 1 0 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "333  1.0  2.0  2.0  1.0  0.0  2.0  12.0   55.977986  89.054349\n",
      "Fitting on : 1 2 2 1 0 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "334  1.0  2.0  2.0  1.0  0.0  3.0  12.0   48.429127  99.963132\n",
      "Fitting on : 1 2 2 1 0 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "335  1.0  2.0  2.0  1.0  0.0  0.0  12.0   53.747241  144.73367\n",
      "Fitting on : 1 2 2 2 1 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "336  1.0  2.0  2.0  2.0  1.0  1.0  12.0    76.97635  63.360727\n",
      "Fitting on : 1 2 2 2 1 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "337  1.0  2.0  2.0  2.0  1.0  2.0  12.0   76.905261  78.454308\n",
      "Fitting on : 1 2 2 2 1 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "338  1.0  2.0  2.0  2.0  1.0  3.0  12.0   76.468121  51.325358\n",
      "Fitting on : 1 2 2 2 1 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "339  1.0  2.0  2.0  2.0  1.0  0.0  12.0   76.974549  69.921855\n",
      "Fitting on : 1 2 2 2 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "340  1.0  2.0  2.0  2.0  2.0  1.0  12.0  133.381075  86.479257\n",
      "Fitting on : 1 2 2 2 2 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "341  1.0  2.0  2.0  2.0  2.0  2.0  12.0  132.958866  85.724133\n",
      "Fitting on : 1 2 2 2 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "342  1.0  2.0  2.0  2.0  2.0  3.0  12.0  133.437888  90.188136\n",
      "Fitting on : 1 2 2 2 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "343  1.0  2.0  2.0  2.0  2.0  0.0  12.0  133.210281  86.655173\n",
      "Fitting on : 1 2 2 2 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "344  1.0  2.0  2.0  2.0  3.0  1.0  12.0  187.524845  440.464196\n",
      "Fitting on : 1 2 2 2 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "345  1.0  2.0  2.0  2.0  3.0  2.0  12.0  186.837247  671.953427\n",
      "Fitting on : 1 2 2 2 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "346  1.0  2.0  2.0  2.0  3.0  3.0  12.0    188.2069  487.651619\n",
      "Fitting on : 1 2 2 2 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "347  1.0  2.0  2.0  2.0  3.0  0.0  12.0  187.773461  688.095531\n",
      "Fitting on : 1 2 2 2 0 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "348  1.0  2.0  2.0  2.0  0.0  1.0  12.0   55.235152  78.032496\n",
      "Fitting on : 1 2 2 2 0 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "349  1.0  2.0  2.0  2.0  0.0  2.0  12.0   54.813286  79.135905\n",
      "Fitting on : 1 2 2 2 0 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "350  1.0  2.0  2.0  2.0  0.0  3.0  12.0   55.054085  86.975094\n",
      "Fitting on : 1 2 2 2 0 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "351  1.0  2.0  2.0  2.0  0.0  0.0  12.0   57.964629  67.848929\n",
      "Fitting on : 1 2 2 3 1 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "352  1.0  2.0  2.0  3.0  1.0  1.0  12.0   76.901947  111.266688\n",
      "Fitting on : 1 2 2 3 1 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "353  1.0  2.0  2.0  3.0  1.0  2.0  12.0   76.944685  98.254268\n",
      "Fitting on : 1 2 2 3 1 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "354  1.0  2.0  2.0  3.0  1.0  3.0  12.0   76.948681  110.554051\n",
      "Fitting on : 1 2 2 3 1 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "355  1.0  2.0  2.0  3.0  1.0  0.0  12.0   76.939482  80.779115\n",
      "Fitting on : 1 2 2 3 2 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "356  1.0  2.0  2.0  3.0  2.0  1.0  12.0  133.220793  87.413998\n",
      "Fitting on : 1 2 2 3 2 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "357  1.0  2.0  2.0  3.0  2.0  2.0  12.0   133.74198  97.326213\n",
      "Fitting on : 1 2 2 3 2 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "358  1.0  2.0  2.0  3.0  2.0  3.0  12.0  133.321215  83.052745\n",
      "Fitting on : 1 2 2 3 2 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train  RMSE_test\n",
      "359  1.0  2.0  2.0  3.0  2.0  0.0  12.0  132.998179  88.249053\n",
      "Fitting on : 1 2 2 3 3 1 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "360  1.0  2.0  2.0  3.0  3.0  1.0  12.0  187.031753  671.904374\n",
      "Fitting on : 1 2 2 3 3 2 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "361  1.0  2.0  2.0  3.0  3.0  2.0  12.0  187.701682  422.977211\n",
      "Fitting on : 1 2 2 3 3 3 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "362  1.0  2.0  2.0  3.0  3.0  3.0  12.0  187.906896  439.593365\n",
      "Fitting on : 1 2 2 3 3 0 12\n",
      "       p    d    q    P    D    Q     m  RMSE_train   RMSE_test\n",
      "363  1.0  2.0  2.0  3.0  3.0  0.0  12.0  187.009787  436.029418\n"
     ]
    }
   ],
   "source": [
    "train=delhi_AQI[:48]\n",
    "test=delhi_AQI[48:61]\n",
    "\n",
    "delhi_AQI.dropna(inplace=True)\n",
    "\n",
    "p = [1,2,3,0]\n",
    "d = [1,2,3,0]\n",
    "q = [1,2,3,0]\n",
    "P = [1,2,3,0]\n",
    "D = [1,2,3,0]\n",
    "Q = [1,2,3,0]\n",
    "m = [12]\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "table = grid_search(p, d, q, P, D, Q, m, train, test)\n",
    "\n",
    "print(table)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "table.to_csv(\"grid-search-results.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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