Forecast Sales of Tractor using ARIMA & SARIMA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0aad261c",
   "metadata": {},
   "source": [
    "### Import Required Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8fec0e20",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize']=15,5\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efba8f1d",
   "metadata": {},
   "source": [
    "### Load & Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1b4fb2ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month-Year</th>\n",
       "      <th>Number of Tractor Sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan-03</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb-03</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar-03</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr-03</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May-03</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month-Year  Number of Tractor Sold\n",
       "0     Jan-03                     141\n",
       "1     Feb-03                     157\n",
       "2     Mar-03                     185\n",
       "3     Apr-03                     199\n",
       "4     May-03                     203"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"D:/PGP IN DATA SCIENCE with Careerera/Data Sets/data-for-time-series/Tractor-Sales.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a68517e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d851e7d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "288"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "999954b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month-Year                object\n",
       "Number of Tractor Sold     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ebf37831",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date']=pd.to_datetime(df['Month-Year'], format='%b-%y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4ddbe772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month-Year                        object\n",
       "Number of Tractor Sold             int64\n",
       "Date                      datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "52bde127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Month-Year</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan-03</td>\n",
       "      <td>141</td>\n",
       "      <td>2003-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb-03</td>\n",
       "      <td>157</td>\n",
       "      <td>2003-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar-03</td>\n",
       "      <td>185</td>\n",
       "      <td>2003-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr-03</td>\n",
       "      <td>199</td>\n",
       "      <td>2003-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May-03</td>\n",
       "      <td>203</td>\n",
       "      <td>2003-05-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month-Year  Sales       Date\n",
       "0     Jan-03    141 2003-01-01\n",
       "1     Feb-03    157 2003-02-01\n",
       "2     Mar-03    185 2003-03-01\n",
       "3     Apr-03    199 2003-04-01\n",
       "4     May-03    203 2003-05-01"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={\"Number of Tractor Sold\":\"Sales\"}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "00171336",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop Month-Year\n",
    "df.drop('Month-Year', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dc44eabc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02-01</th>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-03-01</th>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-04-01</th>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-05-01</th>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Sales\n",
       "Date             \n",
       "2003-01-01    141\n",
       "2003-02-01    157\n",
       "2003-03-01    185\n",
       "2003-04-01    199\n",
       "2003-05-01    203"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set \"Date\" as index\n",
    "df.set_index(\"Date\", inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5cdfbda0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2003-01-01    141\n",
       "2003-02-01    157\n",
       "2003-03-01    185\n",
       "2003-04-01    199\n",
       "2003-05-01    203\n",
       "             ... \n",
       "2014-08-01    848\n",
       "2014-09-01    640\n",
       "2014-10-01    581\n",
       "2014-11-01    519\n",
       "2014-12-01    605\n",
       "Name: Sales, Length: 144, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yt=df.Sales\n",
    "yt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "390b68ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=yt[:\"2014-06-01\"]\n",
    "test=yt[\"2014-07-01\":]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c89bc269",
   "metadata": {},
   "source": [
    "### Check for Stationary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceefc18f",
   "metadata": {},
   "source": [
    "* 1) By Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7c1d1200",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(yt);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "095e40bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(yt, color='blue',label=\"Original\")\n",
    "plt.plot(yt.rolling(window=52,center=False).mean(), color='red',label=\"Rolling Mean\")\n",
    "plt.plot(yt.rolling(window=52,center=False).std(), color='black',label=\"Rolling STD\")\n",
    "plt.legend()\n",
    "plt.title(\"Rolling Mean and Standard Deviation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e88b1cf6",
   "metadata": {},
   "source": [
    "From graph we cannot say our Series in stationary because, we can clearly see Seasonality & Trend. Moving average is increaseing, standard deviation is almost constant."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "786fd434",
   "metadata": {},
   "source": [
    "* 2)  Apply ADfuller test-"
   ]
  },
  {
   "cell_type": "raw",
   "id": "baf30991",
   "metadata": {},
   "source": [
    "Null hypothesis - Series is not stationary\n",
    "Alternative Hypothesis- Series is a stationary\n",
    "Decison Rule : if my p value >= alpha value: We will accept Null hypothesis\n",
    "             : if my p value < alpha value : we will reject null hypothesis and accept alternative Hypothesis\n",
    "             \n",
    "p-value<0.05 (confidence interval=0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "68e059e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.1088250591467286,\n",
       " 0.9952912415248419,\n",
       " 14,\n",
       " 129,\n",
       " {'1%': -3.482087964046026,\n",
       "  '5%': -2.8842185101614626,\n",
       "  '10%': -2.578864381347275},\n",
       " 1082.5357724796872)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "st=adfuller(yt)\n",
    "st"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c5fd987",
   "metadata": {},
   "source": [
    "Write above in more suitable manner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "602a117a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results Of Dickey-Fuller Test\n",
      "Test Statistics                  1.108825\n",
      "p-Value                          0.995291\n",
      "Lags Used                       14.000000\n",
      "Number of Observations used    129.000000\n",
      "Critical value (1%)             -3.482088\n",
      "Critical value (5%)             -2.884219\n",
      "Critical value (10%)            -2.578864\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Results Of Dickey-Fuller Test\")\n",
    "dftest=adfuller(yt)\n",
    "dfoutput=pd.Series(dftest[0:4], index=['Test Statistics','p-Value','Lags Used',\n",
    "                                       'Number of Observations used'])\n",
    "for key,value in dftest[4].items():\n",
    "    dfoutput['Critical value (%s)'%key]=value\n",
    "print(dfoutput)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e76f4a75",
   "metadata": {},
   "source": [
    "As p-value is greater than alpha(0.05), Series is not Stationary."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22aa0c04",
   "metadata": {},
   "source": [
    "We can take difference of series and aaply that to adfuller test % check the p-value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a47d1ed9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2003-04-01     58.0\n",
       "2003-05-01     46.0\n",
       "2003-06-01      4.0\n",
       "2003-07-01      8.0\n",
       "2003-08-01      4.0\n",
       "              ...  \n",
       "2014-08-01     55.0\n",
       "2014-09-01   -109.0\n",
       "2014-10-01   -290.0\n",
       "2014-11-01   -329.0\n",
       "2014-12-01    -35.0\n",
       "Name: Sales, Length: 141, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yt1=yt.diff(3).dropna()\n",
    "yt1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dbc3064a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results Of Dickey-Fuller Test\n",
      "Test Statistics                 -2.936724\n",
      "p-Value                          0.041241\n",
      "Lags Used                       10.000000\n",
      "Number of Observations used    130.000000\n",
      "Critical value (1%)             -3.481682\n",
      "Critical value (5%)             -2.884042\n",
      "Critical value (10%)            -2.578770\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Results Of Dickey-Fuller Test\")\n",
    "dftest=adfuller(yt1)\n",
    "dfoutput=pd.Series(dftest[0:4], index=['Test Statistics','p-Value','Lags Used',\n",
    "                                       'Number of Observations used'])\n",
    "for key,value in dftest[4].items():\n",
    "    dfoutput['Critical value (%s)'%key]=value\n",
    "print(dfoutput)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "049b1ac6",
   "metadata": {},
   "source": [
    "After taking 3 difference p-value is less than alpha(0.05), hence series is approaching toward stationary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "08d8c796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(yt1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe880be1",
   "metadata": {},
   "source": [
    "Now we can see the Trend but not like before, Hence Series is aprroaching to Stationary."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "316e9c9f",
   "metadata": {},
   "source": [
    "We can also try log tranformation and then check the p-value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "922d953f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2003-03-01    0.271596\n",
       "2003-04-01    0.237059\n",
       "2003-05-01    0.092850\n",
       "2003-06-01   -0.051558\n",
       "2003-07-01    0.019513\n",
       "                ...   \n",
       "2014-08-01    0.124142\n",
       "2014-09-01   -0.308174\n",
       "2014-10-01   -0.378130\n",
       "2014-11-01   -0.209564\n",
       "2014-12-01    0.040478\n",
       "Name: Sales, Length: 142, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yt1=np.log(yt).diff(2).dropna()\n",
    "yt1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0cb6f6d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results Of Dickey-Fuller Test\n",
      "Test Statistics                 -3.200722\n",
      "p-Value                          0.019943\n",
      "Lags Used                       11.000000\n",
      "Number of Observations used    130.000000\n",
      "Critical value (1%)             -3.481682\n",
      "Critical value (5%)             -2.884042\n",
      "Critical value (10%)            -2.578770\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Results Of Dickey-Fuller Test\")\n",
    "dftest=adfuller(yt1)\n",
    "dfoutput=pd.Series(dftest[0:4], index=['Test Statistics','p-Value','Lags Used',\n",
    "                                       'Number of Observations used'])\n",
    "for key,value in dftest[4].items():\n",
    "    dfoutput['Critical value (%s)'%key]=value\n",
    "print(dfoutput)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "39d49a90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(yt1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a192a88",
   "metadata": {},
   "source": [
    "Now the trend is gone and p-value(0.019943) is less than alpha value(0.05) hence Series Stationary."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4c7ba28",
   "metadata": {},
   "source": [
    "#### To Know p,d,q,P,D,Q,Period. Plot Autocorelation & Partial-Autocorelation graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b6c7fc34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.tsa.api as smt\n",
    "fig,axes=plt.subplots(1,2,sharex=False,sharey=False)\n",
    "fig.set_figwidth(10)\n",
    "fig.set_figheight(4)\n",
    "smt.graphics.plot_acf(yt1, lags=10, ax=axes[0], alpha=0.2) # Autocorelation\n",
    "smt.graphics.plot_acf(yt1, lags=10, ax=axes[1], alpha=0.5) # Partial-Autocorelation\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1912d63",
   "metadata": {},
   "source": [
    "* q=After how many Lags Series is either 0 or Negative in Autocorelation\n",
    "* p=After how many Lags Series is either 0 or Negative in Partial-Autocorelation\n",
    "* d=After how many difference the series become Stationary\n",
    "* Period- After how many Months Pattern is repeating.\n",
    "* To get P,D,Q create new series using our series which is grouped up with 12 years(in our case) ans sum up each year and do the same thing which we did while calculating p,q,d."
   ]
  },
  {
   "cell_type": "raw",
   "id": "dbb1c567",
   "metadata": {},
   "source": [
    "From Graph,\n",
    "q=1\n",
    "p=1\n",
    "d=2\n",
    "p=12\n",
    "P=\n",
    "D=\n",
    "Q="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2eadb3fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2003-01-01    4.948760\n",
       "2003-02-01    5.056246\n",
       "2003-03-01    5.220356\n",
       "2003-04-01    5.293305\n",
       "2003-05-01    5.313206\n",
       "                ...   \n",
       "2014-08-01    6.742881\n",
       "2014-09-01    6.461468\n",
       "2014-10-01    6.364751\n",
       "2014-11-01    6.251904\n",
       "2014-12-01    6.405228\n",
       "Name: Sales, Length: 144, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yt_log=np.log(yt).dropna()\n",
    "yt_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d9125f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=yt_log[:\"2014-06-01\"]\n",
    "test=yt_log[\"2014-07-01\":]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "250adbd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2003-01-01    4.948760\n",
       "2003-02-01    5.056246\n",
       "2003-03-01    5.220356\n",
       "2003-04-01    5.293305\n",
       "2003-05-01    5.313206\n",
       "                ...   \n",
       "2014-02-01    6.253829\n",
       "2014-03-01    6.375025\n",
       "2014-04-01    6.565265\n",
       "2014-05-01    6.675823\n",
       "2014-06-01    6.618739\n",
       "Name: Sales, Length: 138, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a8242f6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2014-07-01    6.769642\n",
       "2014-08-01    6.742881\n",
       "2014-09-01    6.461468\n",
       "2014-10-01    6.364751\n",
       "2014-11-01    6.251904\n",
       "2014-12-01    6.405228\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e1a8ae1b",
   "metadata": {},
   "source": [
    "Model takes 2 paramater,\n",
    "order=(p,d,q)  & seasonal order=(P,D,Q,Period)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57ee827d",
   "metadata": {},
   "source": [
    "#### Now Create model & Train it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3a79904a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "model=sm.tsa.statespace.SARIMAX(train, order=(1,2,1), seasonal_order=(1,0,1,12),\n",
    "                               enforce_stationarity=False,\n",
    "                                enforce_invertibility=False).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fa969afd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                     SARIMAX Results                                      \n",
      "==========================================================================================\n",
      "Dep. Variable:                              Sales   No. Observations:                  136\n",
      "Model:             SARIMAX(1, 2, 1)x(1, 0, 1, 12)   Log Likelihood                 174.404\n",
      "Date:                            Tue, 20 Dec 2022   AIC                           -338.808\n",
      "Time:                                    12:05:08   BIC                           -324.871\n",
      "Sample:                                03-01-2003   HQIC                          -333.148\n",
      "                                     - 06-01-2014                                         \n",
      "Covariance Type:                              opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1         -0.1253      0.088     -1.417      0.157      -0.299       0.048\n",
      "ma.L1         -1.0000    182.078     -0.005      0.996    -357.867     355.867\n",
      "ar.S.L12       1.0046      0.018     56.591      0.000       0.970       1.039\n",
      "ma.S.L12      -0.5302      0.097     -5.445      0.000      -0.721      -0.339\n",
      "sigma2         0.0030      0.540      0.005      0.996      -1.055       1.061\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.25   Jarque-Bera (JB):                 0.11\n",
      "Prob(Q):                              0.62   Prob(JB):                         0.95\n",
      "Heteroskedasticity (H):               0.49   Skew:                            -0.07\n",
      "Prob(H) (two-sided):                  0.03   Kurtosis:                         3.03\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "68410745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-07-01    1.073361\n",
       "2014-08-01    1.128906\n",
       "2014-09-01    0.740949\n",
       "2014-10-01    0.640452\n",
       "2014-11-01    0.794180\n",
       "2014-12-01    1.061687\n",
       "Freq: MS, Name: predicted_mean, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_fcast=np.exp(model.forecast(6))\n",
    "y_fcast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f71ef0cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2014-07-01    1.098361\n",
       "2014-08-01    1.132176\n",
       "2014-09-01    0.734788\n",
       "2014-10-01    0.685142\n",
       "2014-11-01    0.810937\n",
       "2014-12-01    1.041308\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_orig=np.exp(test)\n",
    "y_orig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d206e403",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE 0.0005611464594526579\n"
     ]
    }
   ],
   "source": [
    "print(\"MSE\", np.mean((y_fcast-y_orig)**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8db2a3e3",
   "metadata": {},
   "source": [
    "To choose better values of order & seasonal_order use itertool method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bb73b82c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "p=[0,1,2]\n",
    "d=[0,1,2]\n",
    "q=[1,2,3]\n",
    "P=[0,1,2]\n",
    "D=[0]\n",
    "Q=[0,1,2]\n",
    "period=12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "879c1918",
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "pdq=list(itertools.product(p,d,q))\n",
    "seasonal_pdq= [(x[0], x[1], x[2], 12) for x in list(itertools.product(P,D,Q))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "015d4a44",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\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"
     ]
    }
   ],
   "source": [
    "best_pdq=None\n",
    "best_PDQ=None\n",
    "best_mse=np.inf\n",
    "\n",
    "for i in pdq:\n",
    "    for j in seasonal_pdq:\n",
    "        model=sm.tsa.statespace.SARIMAX(train, order=i, seasonal_order=j,\n",
    "                                        enforce_stationarity=False,\n",
    "                                        enforce_invertibility=False).fit()\n",
    "        fcast=np.exp(model.forecast(6))\n",
    "        test=np.exp(test)\n",
    "        mse=np.mean((fcast-test)**2)\n",
    "        if mse<best_mse:\n",
    "            best_pdq=i\n",
    "            best_PDQ=j\n",
    "            best_mse=mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "18efa96b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 1)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_pdq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a12f43ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 0, 12)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_PDQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7af52530",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04175266426616117"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_mse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4002c0c8",
   "metadata": {},
   "source": [
    "Lets use above values to train our mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "343f0ef9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    }
   ],
   "source": [
    "model=sm.tsa.statespace.SARIMAX(train, order=(0, 0, 1), seasonal_order=(0, 0, 0, 12),\n",
    "                               enforce_stationarity=False,\n",
    "                                enforce_invertibility=False).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9389cb2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-07-01    33.179216\n",
       "2014-08-01     1.000000\n",
       "2014-09-01     1.000000\n",
       "2014-10-01     1.000000\n",
       "2014-11-01     1.000000\n",
       "2014-12-01     1.000000\n",
       "Freq: MS, Name: predicted_mean, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcast=model.forecast(6)\n",
    "np.exp(fcast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4bade942",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2014-07-01    871.0\n",
       "2014-08-01    848.0\n",
       "2014-09-01    640.0\n",
       "2014-10-01    581.0\n",
       "2014-11-01    519.0\n",
       "2014-12-01    605.0\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "456f327b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE 466202.27770284243\n"
     ]
    }
   ],
   "source": [
    "print(\"MSE\",np.mean((np.exp(fcast)-np.exp(test))**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fc4e084",
   "metadata": {},
   "source": [
    "#### Actuual vs Predicted Values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1ce3f2e",
   "metadata": {},
   "source": [
    "d=pd.DataFrame(fcast.reset_index())\n",
    "d.columns=[\"Date\",\"Predicted\"]\n",
    "\n",
    "d1=pd.DataFrame(test.reset_index())\n",
    "d1.columns=[\"Date\",\"Actual\"]\n",
    "final=pd.merge(d,d1,on=\"Date\",how=\"inner\")\n",
    "plt.plot(final[\"Date\"],final[\"Predicted\"], label=\"Predicted Value\", color=\"red\")\n",
    "plt.plot(final[\"Date\"],final[\"Actual\"], label=\"Actual Values\",color=\"green\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05853ed5",
   "metadata": {},
   "source": [
    "Forecast for Unseen Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd36ef9a",
   "metadata": {},
   "source": [
    "fcast=model.forecast(18)\n",
    "fcast"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e5b5138",
   "metadata": {},
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(train, label='Tractor Sales')\n",
    "plt.plot(ets_fit.fittedvalues, label='Fittel Value')\n",
    "plt.plot(fcast, label='Forecasted Sale')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
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