9_VAR_Vect_AutoReg_VARMA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vector AutoRegression VAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "# Load specific forecasting tools\n",
    "\n",
    "from statsmodels.tsa.api import VAR#, DynamicVAR\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tools.eval_measures import rmse\n",
    "\n",
    "# Ignore harmless warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Load datasets\n",
    "df = pd.read_csv('./UDEMY_TSA_FINAL/Data/M2SLMoneyStock.csv',index_col=0, parse_dates=True)\n",
    "df.index.freq = 'MS'\n",
    "\n",
    "sp = pd.read_csv('./UDEMY_TSA_FINAL/Data/PCEPersonalSpending.csv',index_col=0, parse_dates=True)\n",
    "sp.index.freq = 'MS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(             Money\n",
       " Date              \n",
       " 1995-01-01  3492.4\n",
       " 1995-02-01  3489.9\n",
       " 1995-03-01  3491.1\n",
       " 1995-04-01  3499.2\n",
       " 1995-05-01  3524.2,\n",
       "             Spending\n",
       " Date                \n",
       " 1995-01-01    4851.2\n",
       " 1995-02-01    4850.8\n",
       " 1995-03-01    4885.4\n",
       " 1995-04-01    4890.2\n",
       " 1995-05-01    4933.1)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(), sp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.join(sp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money</th>\n",
       "      <th>Spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>3492.4</td>\n",
       "      <td>4851.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-02-01</th>\n",
       "      <td>3489.9</td>\n",
       "      <td>4850.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-03-01</th>\n",
       "      <td>3491.1</td>\n",
       "      <td>4885.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-04-01</th>\n",
       "      <td>3499.2</td>\n",
       "      <td>4890.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-01</th>\n",
       "      <td>3524.2</td>\n",
       "      <td>4933.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Money  Spending\n",
       "Date                        \n",
       "1995-01-01  3492.4    4851.2\n",
       "1995-02-01  3489.9    4850.8\n",
       "1995-03-01  3491.1    4885.4\n",
       "1995-04-01  3499.2    4890.2\n",
       "1995-05-01  3524.2    4933.1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(252, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### plotting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'M2 money stock vs personal consumption expenditures'}, xlabel='Date', ylabel='billions of dollars'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(ylabel=\"billions of dollars\",title=\"M2 money stock vs personal consumption expenditures\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### stationary test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adf_test(series,title=''):\n",
    "    \"\"\"\n",
    "    Pass in a time series and an optional title, returns an ADF report\n",
    "    \"\"\"\n",
    "    print(f'Augmented Dickey-Fuller Test: {title}')\n",
    "    result = adfuller(series.dropna(),autolag='AIC') # .dropna() handles differenced data\n",
    "    \n",
    "    labels = ['ADF test statistic','p-value','# lags used','# observations']\n",
    "    out = pd.Series(result[0:4],index=labels)\n",
    "\n",
    "    for key,val in result[4].items():\n",
    "        out[f'critical value ({key})']=val\n",
    "        \n",
    "    print(out.to_string())          # .to_string() removes the line \"dtype: float64\"\n",
    "    \n",
    "    if result[1] <= 0.05:\n",
    "        print(\"Strong evidence against the null hypothesis\")\n",
    "        print(\"Reject the null hypothesis\")\n",
    "        print(\"Data has no unit root and is stationary\")\n",
    "    else:\n",
    "        print(\"Weak evidence against the null hypothesis\")\n",
    "        print(\"Fail to reject the null hypothesis\")\n",
    "        print(\"Data has a unit root and is non-stationary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: Money\n",
      "ADF test statistic        4.239022\n",
      "p-value                   1.000000\n",
      "# lags used               4.000000\n",
      "# observations          247.000000\n",
      "critical value (1%)      -3.457105\n",
      "critical value (5%)      -2.873314\n",
      "critical value (10%)     -2.573044\n",
      "Weak evidence against the null hypothesis\n",
      "Fail to reject the null hypothesis\n",
      "Data has a unit root and is non-stationary\n"
     ]
    }
   ],
   "source": [
    "adf_test(df[\"Money\"],title='Money')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: Money\n",
      "ADF test statistic        0.149796\n",
      "p-value                   0.969301\n",
      "# lags used               3.000000\n",
      "# observations          248.000000\n",
      "critical value (1%)      -3.456996\n",
      "critical value (5%)      -2.873266\n",
      "critical value (10%)     -2.573019\n",
      "Weak evidence against the null hypothesis\n",
      "Fail to reject the null hypothesis\n",
      "Data has a unit root and is non-stationary\n"
     ]
    }
   ],
   "source": [
    "adf_test(df[\"Spending\"],title='Money')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transformed=df.diff() #shiftted one "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money</th>\n",
       "      <th>Spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-02-01</th>\n",
       "      <td>-2.5</td>\n",
       "      <td>-0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-03-01</th>\n",
       "      <td>1.2</td>\n",
       "      <td>34.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-04-01</th>\n",
       "      <td>8.1</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-01</th>\n",
       "      <td>25.0</td>\n",
       "      <td>42.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>51.5</td>\n",
       "      <td>38.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>57.0</td>\n",
       "      <td>-1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>33.9</td>\n",
       "      <td>23.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>89.7</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>58.5</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Money  Spending\n",
       "Date                       \n",
       "1995-01-01    NaN       NaN\n",
       "1995-02-01   -2.5      -0.4\n",
       "1995-03-01    1.2      34.6\n",
       "1995-04-01    8.1       4.8\n",
       "1995-05-01   25.0      42.9\n",
       "...           ...       ...\n",
       "2015-08-01   51.5      38.6\n",
       "2015-09-01   57.0      -1.2\n",
       "2015-10-01   33.9      23.3\n",
       "2015-11-01   89.7      34.0\n",
       "2015-12-01   58.5      19.0\n",
       "\n",
       "[252 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transformed=df_transformed.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money</th>\n",
       "      <th>Spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-02-01</th>\n",
       "      <td>-2.5</td>\n",
       "      <td>-0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-03-01</th>\n",
       "      <td>1.2</td>\n",
       "      <td>34.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-04-01</th>\n",
       "      <td>8.1</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-01</th>\n",
       "      <td>25.0</td>\n",
       "      <td>42.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>51.5</td>\n",
       "      <td>38.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>57.0</td>\n",
       "      <td>-1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>33.9</td>\n",
       "      <td>23.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>89.7</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>58.5</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Money  Spending\n",
       "Date                       \n",
       "1995-01-01    NaN       NaN\n",
       "1995-02-01   -2.5      -0.4\n",
       "1995-03-01    1.2      34.6\n",
       "1995-04-01    8.1       4.8\n",
       "1995-05-01   25.0      42.9\n",
       "...           ...       ...\n",
       "2015-08-01   51.5      38.6\n",
       "2015-09-01   57.0      -1.2\n",
       "2015-10-01   33.9      23.3\n",
       "2015-11-01   89.7      34.0\n",
       "2015-12-01   58.5      19.0\n",
       "\n",
       "[252 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: \n",
      "ADF test statistic       -2.057404\n",
      "p-value                   0.261984\n",
      "# lags used              15.000000\n",
      "# observations          235.000000\n",
      "critical value (1%)      -3.458487\n",
      "critical value (5%)      -2.873919\n",
      "critical value (10%)     -2.573367\n",
      "Weak evidence against the null hypothesis\n",
      "Fail to reject the null hypothesis\n",
      "Data has a unit root and is non-stationary\n",
      "Augmented Dickey-Fuller Test: \n",
      "ADF test statistic     -7.226974e+00\n",
      "p-value                 2.041027e-10\n",
      "# lags used             2.000000e+00\n",
      "# observations          2.480000e+02\n",
      "critical value (1%)    -3.456996e+00\n",
      "critical value (5%)    -2.873266e+00\n",
      "critical value (10%)   -2.573019e+00\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "#now check stationary again\n",
    "\n",
    "adf_test(df_transformed[\"Money\"]), #this is still not stationary\n",
    "\n",
    "adf_test(df_transformed[\"Spending\"]) #this is stationary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we do one more diff to make money db stationary\n",
    "\n",
    "df_transformed=df_transformed.diff().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: \n",
      "ADF test statistic     -7.077471e+00\n",
      "p-value                 4.760675e-10\n",
      "# lags used             1.400000e+01\n",
      "# observations          2.350000e+02\n",
      "critical value (1%)    -3.458487e+00\n",
      "critical value (5%)    -2.873919e+00\n",
      "critical value (10%)   -2.573367e+00\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n",
      "Augmented Dickey-Fuller Test: \n",
      "ADF test statistic     -8.760145e+00\n",
      "p-value                 2.687900e-14\n",
      "# lags used             8.000000e+00\n",
      "# observations          2.410000e+02\n",
      "critical value (1%)    -3.457779e+00\n",
      "critical value (5%)    -2.873609e+00\n",
      "critical value (10%)   -2.573202e+00\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "#check the stationary again\n",
    "adf_test(df_transformed[\"Money\"]), #this is now stationary\n",
    "\n",
    "adf_test(df_transformed[\"Spending\"]) # this is stationary too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money</th>\n",
       "      <th>Spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-03-01</th>\n",
       "      <td>3.7</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-04-01</th>\n",
       "      <td>6.9</td>\n",
       "      <td>-29.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-01</th>\n",
       "      <td>16.9</td>\n",
       "      <td>38.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-06-01</th>\n",
       "      <td>-0.3</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-07-01</th>\n",
       "      <td>-6.2</td>\n",
       "      <td>-51.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Money  Spending\n",
       "Date                       \n",
       "1995-03-01    3.7      35.0\n",
       "1995-04-01    6.9     -29.8\n",
       "1995-05-01   16.9      38.1\n",
       "1995-06-01   -0.3       1.5\n",
       "1995-07-01   -6.2     -51.7"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed.head()#stationary dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### train/test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using 12 months\n",
    "nobs=12\n",
    "train,test=df_transformed[0:-nobs],df_transformed[-nobs:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((238, 2), (12, 2))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VAR Model Order Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Order= 1\n",
      "AIC: 14.178610495220896\n",
      "BIC: 14.266409486135709\n",
      "\n",
      "Order= 2\n",
      "AIC: 14.178610495220896\n",
      "BIC: 14.266409486135709\n",
      "\n",
      "Order= 3\n",
      "AIC: 14.178610495220896\n",
      "BIC: 14.266409486135709\n",
      "\n",
      "Order= 4\n",
      "AIC: 14.178610495220896\n",
      "BIC: 14.266409486135709\n",
      "\n",
      "Order= 5\n",
      "AIC: 14.178610495220896\n",
      "BIC: 14.266409486135709\n",
      "\n",
      "Order= 6\n",
      "AIC: 14.178610495220896\n",
      "BIC: 14.266409486135709\n",
      "\n",
      "Order= 7\n",
      "AIC: 14.178610495220896\n",
      "BIC: 14.266409486135709\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in [1,2,3,4,5,6,7]:\n",
    "    model=VAR(train)\n",
    "    results=model.fit()\n",
    "    print(\"Order=\",i)\n",
    "    print(\"AIC:\", results.aic)\n",
    "    print(\"BIC:\",results.bic)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Order = 1\n",
      "AIC:  14.178610495220896\n",
      "BIC:  14.266409486135709\n",
      "\n",
      "Order = 2\n",
      "AIC:  13.955189367163703\n",
      "BIC:  14.101961901274956\n",
      "\n",
      "Order = 3\n",
      "AIC:  13.849518291541038\n",
      "BIC:  14.055621258341116\n",
      "\n",
      "Order = 4\n",
      "AIC:  13.827950574458281\n",
      "BIC:  14.093744506408875\n",
      "\n",
      "Order = 5\n",
      "AIC:  13.78730034460964\n",
      "BIC:  14.113149468980652\n",
      "\n",
      "Order = 6\n",
      "AIC:  13.799076756885809\n",
      "BIC:  14.185349048538068\n",
      "\n",
      "Order = 7\n",
      "AIC:  13.79763872791397\n",
      "BIC:  14.24470596304667\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model=VAR(train)\n",
    "for i in [1,2,3,4,5,6,7]:\n",
    "    results=model.fit(i)\n",
    "    print('Order =', i)\n",
    "    print('AIC: ', results.aic)\n",
    "    print('BIC: ', results.bic)\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### var 5 seems returing the lowest scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Money', 'Spending']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.endog_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting VAR 5 Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Summary of Regression Results   \n",
       "==================================\n",
       "Model:                         VAR\n",
       "Method:                        OLS\n",
       "Date:           Thu, 20, Jul, 2023\n",
       "Time:                     14:43:37\n",
       "--------------------------------------------------------------------\n",
       "No. of Equations:         2.00000    BIC:                    14.1131\n",
       "Nobs:                     233.000    HQIC:                   13.9187\n",
       "Log likelihood:          -2245.45    FPE:                    972321.\n",
       "AIC:                      13.7873    Det(Omega_mle):         886628.\n",
       "--------------------------------------------------------------------\n",
       "Results for equation Money\n",
       "==============================================================================\n",
       "                 coefficient       std. error           t-stat            prob\n",
       "------------------------------------------------------------------------------\n",
       "const               0.516683         1.782238            0.290           0.772\n",
       "L1.Money           -0.646232         0.068177           -9.479           0.000\n",
       "L1.Spending        -0.107411         0.051388           -2.090           0.037\n",
       "L2.Money           -0.497482         0.077749           -6.399           0.000\n",
       "L2.Spending        -0.192202         0.068613           -2.801           0.005\n",
       "L3.Money           -0.234442         0.081004           -2.894           0.004\n",
       "L3.Spending        -0.178099         0.074288           -2.397           0.017\n",
       "L4.Money           -0.295531         0.075294           -3.925           0.000\n",
       "L4.Spending        -0.035564         0.069664           -0.511           0.610\n",
       "L5.Money           -0.162399         0.066700           -2.435           0.015\n",
       "L5.Spending        -0.058449         0.051357           -1.138           0.255\n",
       "==============================================================================\n",
       "\n",
       "Results for equation Spending\n",
       "==============================================================================\n",
       "                 coefficient       std. error           t-stat            prob\n",
       "------------------------------------------------------------------------------\n",
       "const               0.203469         2.355446            0.086           0.931\n",
       "L1.Money            0.188105         0.090104            2.088           0.037\n",
       "L1.Spending        -0.878970         0.067916          -12.942           0.000\n",
       "L2.Money            0.053017         0.102755            0.516           0.606\n",
       "L2.Spending        -0.625313         0.090681           -6.896           0.000\n",
       "L3.Money           -0.022172         0.107057           -0.207           0.836\n",
       "L3.Spending        -0.389041         0.098180           -3.963           0.000\n",
       "L4.Money           -0.170456         0.099510           -1.713           0.087\n",
       "L4.Spending        -0.245435         0.092069           -2.666           0.008\n",
       "L5.Money           -0.083165         0.088153           -0.943           0.345\n",
       "L5.Spending        -0.181699         0.067874           -2.677           0.007\n",
       "==============================================================================\n",
       "\n",
       "Correlation matrix of residuals\n",
       "               Money  Spending\n",
       "Money       1.000000 -0.267934\n",
       "Spending   -0.267934  1.000000\n",
       "\n"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results=model.fit(5)\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting next 12 months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lag_order=results.k_ar\n",
    "lag_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-16.99527634,  36.14982003],\n",
       "       [ -3.17403756, -11.45029844],\n",
       "       [ -0.377725  ,  -6.68496939],\n",
       "       [ -2.60223305,   5.47945777],\n",
       "       [  4.228557  ,  -2.44336505],\n",
       "       [  1.55939341,   0.38763902],\n",
       "       [ -0.99841027,   3.88368011],\n",
       "       [  0.36451042,  -2.3561014 ],\n",
       "       [ -1.21062726,  -1.22414652],\n",
       "       [  0.22587712,   0.786927  ],\n",
       "       [  1.33893884,   0.18097449],\n",
       "       [ -0.21858453,   0.21275046]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z=results.forecast(y=train.values[-lag_order:],steps=12)\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money</th>\n",
       "      <th>Spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>-15.5</td>\n",
       "      <td>-26.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>56.1</td>\n",
       "      <td>52.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>-102.8</td>\n",
       "      <td>39.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>30.9</td>\n",
       "      <td>-40.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>-15.8</td>\n",
       "      <td>38.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>14.0</td>\n",
       "      <td>-34.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>6.7</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>-0.7</td>\n",
       "      <td>-8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>5.5</td>\n",
       "      <td>-39.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>-23.1</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>55.8</td>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>-31.2</td>\n",
       "      <td>-15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Money  Spending\n",
       "Date                       \n",
       "2015-01-01  -15.5     -26.6\n",
       "2015-02-01   56.1      52.4\n",
       "2015-03-01 -102.8      39.5\n",
       "2015-04-01   30.9     -40.4\n",
       "2015-05-01  -15.8      38.8\n",
       "2015-06-01   14.0     -34.1\n",
       "2015-07-01    6.7       6.9\n",
       "2015-08-01   -0.7      -8.5\n",
       "2015-09-01    5.5     -39.8\n",
       "2015-10-01  -23.1      24.5\n",
       "2015-11-01   55.8      10.7\n",
       "2015-12-01  -31.2     -15.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money2d</th>\n",
       "      <th>Spending2d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>-16.995276</td>\n",
       "      <td>36.149820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>-3.174038</td>\n",
       "      <td>-11.450298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>-0.377725</td>\n",
       "      <td>-6.684969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>-2.602233</td>\n",
       "      <td>5.479458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>4.228557</td>\n",
       "      <td>-2.443365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>1.559393</td>\n",
       "      <td>0.387639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>-0.998410</td>\n",
       "      <td>3.883680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>0.364510</td>\n",
       "      <td>-2.356101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>-1.210627</td>\n",
       "      <td>-1.224147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>0.225877</td>\n",
       "      <td>0.786927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>1.338939</td>\n",
       "      <td>0.180974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>-0.218585</td>\n",
       "      <td>0.212750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Money2d  Spending2d\n",
       "2015-01-01 -16.995276   36.149820\n",
       "2015-02-01  -3.174038  -11.450298\n",
       "2015-03-01  -0.377725   -6.684969\n",
       "2015-04-01  -2.602233    5.479458\n",
       "2015-05-01   4.228557   -2.443365\n",
       "2015-06-01   1.559393    0.387639\n",
       "2015-07-01  -0.998410    3.883680\n",
       "2015-08-01   0.364510   -2.356101\n",
       "2015-09-01  -1.210627   -1.224147\n",
       "2015-10-01   0.225877    0.786927\n",
       "2015-11-01   1.338939    0.180974\n",
       "2015-12-01  -0.218585    0.212750"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx=pd.date_range(\"1/1/2015\",periods=12,freq=\"MS\")\n",
    "df_forecast=pd.DataFrame(z,index=idx,columns=[\"Money2d\",\"Spending2d\"])\n",
    "df_forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Invering the diff transformation to real values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the most recent first difference from the training side of the original dataset to the forecast cumulative sum\n",
    "df_forecast['Money1d'] = (df['Money'].iloc[-nobs-1]-df['Money'].iloc[-nobs-2]) + df_forecast['Money2d'].cumsum()\n",
    "\n",
    "# Now build the forecast values from the first difference set\n",
    "df_forecast['MoneyForecast'] = df['Money'].iloc[-nobs-1] + df_forecast['Money1d'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the most recent first difference from the training side of the original dataset to the forecast cumulative sum\n",
    "df_forecast['Spending1d'] = (df['Spending'].iloc[-nobs-1]-df['Spending'].iloc[-nobs-2]) + df_forecast['Spending2d'].cumsum()\n",
    "\n",
    "# Now build the forecast values from the first difference set\n",
    "df_forecast['SpendingForecast'] = df['Spending'].iloc[-nobs-1] + df_forecast['Spending1d'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money2d</th>\n",
       "      <th>Spending2d</th>\n",
       "      <th>Money1d</th>\n",
       "      <th>MoneyForecast</th>\n",
       "      <th>Spending1d</th>\n",
       "      <th>SpendingForecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>-16.995276</td>\n",
       "      <td>36.149820</td>\n",
       "      <td>61.604724</td>\n",
       "      <td>11731.704724</td>\n",
       "      <td>46.749820</td>\n",
       "      <td>12108.749820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>-3.174038</td>\n",
       "      <td>-11.450298</td>\n",
       "      <td>58.430686</td>\n",
       "      <td>11790.135410</td>\n",
       "      <td>35.299522</td>\n",
       "      <td>12144.049342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>-0.377725</td>\n",
       "      <td>-6.684969</td>\n",
       "      <td>58.052961</td>\n",
       "      <td>11848.188371</td>\n",
       "      <td>28.614552</td>\n",
       "      <td>12172.663894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>-2.602233</td>\n",
       "      <td>5.479458</td>\n",
       "      <td>55.450728</td>\n",
       "      <td>11903.639099</td>\n",
       "      <td>34.094010</td>\n",
       "      <td>12206.757904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>4.228557</td>\n",
       "      <td>-2.443365</td>\n",
       "      <td>59.679285</td>\n",
       "      <td>11963.318384</td>\n",
       "      <td>31.650645</td>\n",
       "      <td>12238.408549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>1.559393</td>\n",
       "      <td>0.387639</td>\n",
       "      <td>61.238678</td>\n",
       "      <td>12024.557062</td>\n",
       "      <td>32.038284</td>\n",
       "      <td>12270.446833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>-0.998410</td>\n",
       "      <td>3.883680</td>\n",
       "      <td>60.240268</td>\n",
       "      <td>12084.797331</td>\n",
       "      <td>35.921964</td>\n",
       "      <td>12306.368797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>0.364510</td>\n",
       "      <td>-2.356101</td>\n",
       "      <td>60.604779</td>\n",
       "      <td>12145.402109</td>\n",
       "      <td>33.565863</td>\n",
       "      <td>12339.934659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>-1.210627</td>\n",
       "      <td>-1.224147</td>\n",
       "      <td>59.394151</td>\n",
       "      <td>12204.796261</td>\n",
       "      <td>32.341716</td>\n",
       "      <td>12372.276375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>0.225877</td>\n",
       "      <td>0.786927</td>\n",
       "      <td>59.620028</td>\n",
       "      <td>12264.416289</td>\n",
       "      <td>33.128643</td>\n",
       "      <td>12405.405019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>1.338939</td>\n",
       "      <td>0.180974</td>\n",
       "      <td>60.958967</td>\n",
       "      <td>12325.375256</td>\n",
       "      <td>33.309618</td>\n",
       "      <td>12438.714636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>-0.218585</td>\n",
       "      <td>0.212750</td>\n",
       "      <td>60.740383</td>\n",
       "      <td>12386.115639</td>\n",
       "      <td>33.522368</td>\n",
       "      <td>12472.237004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Money2d  Spending2d    Money1d  MoneyForecast  Spending1d  \\\n",
       "2015-01-01 -16.995276   36.149820  61.604724   11731.704724   46.749820   \n",
       "2015-02-01  -3.174038  -11.450298  58.430686   11790.135410   35.299522   \n",
       "2015-03-01  -0.377725   -6.684969  58.052961   11848.188371   28.614552   \n",
       "2015-04-01  -2.602233    5.479458  55.450728   11903.639099   34.094010   \n",
       "2015-05-01   4.228557   -2.443365  59.679285   11963.318384   31.650645   \n",
       "2015-06-01   1.559393    0.387639  61.238678   12024.557062   32.038284   \n",
       "2015-07-01  -0.998410    3.883680  60.240268   12084.797331   35.921964   \n",
       "2015-08-01   0.364510   -2.356101  60.604779   12145.402109   33.565863   \n",
       "2015-09-01  -1.210627   -1.224147  59.394151   12204.796261   32.341716   \n",
       "2015-10-01   0.225877    0.786927  59.620028   12264.416289   33.128643   \n",
       "2015-11-01   1.338939    0.180974  60.958967   12325.375256   33.309618   \n",
       "2015-12-01  -0.218585    0.212750  60.740383   12386.115639   33.522368   \n",
       "\n",
       "            SpendingForecast  \n",
       "2015-01-01      12108.749820  \n",
       "2015-02-01      12144.049342  \n",
       "2015-03-01      12172.663894  \n",
       "2015-04-01      12206.757904  \n",
       "2015-05-01      12238.408549  \n",
       "2015-06-01      12270.446833  \n",
       "2015-07-01      12306.368797  \n",
       "2015-08-01      12339.934659  \n",
       "2015-09-01      12372.276375  \n",
       "2015-10-01      12405.405019  \n",
       "2015-11-01      12438.714636  \n",
       "2015-12-01      12472.237004  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_forecast\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plotting the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot_forecast(12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### plot predicting vs test values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Money\"][-nobs:].plot(legend=True)\n",
    "df_forecast[\"MoneyForecast\"].plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Spending'][-nobs:].plot(figsize=(12,5),legend=True).autoscale(axis='x',tight=True)\n",
    "df_forecast['SpendingForecast'].plot(legend=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Money VAR(5) RMSE: 43.710\n"
     ]
    }
   ],
   "source": [
    "RMSE1=rmse(df[\"Money\"][-nobs:],df_forecast[\"MoneyForecast\"])\n",
    "#RMSE1\n",
    "print(f'Money VAR(5) RMSE: {RMSE1:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spending VAR(5) RMSE: 37.001\n"
     ]
    }
   ],
   "source": [
    "RMSE2 = rmse(df['Spending'][-nobs:], df_forecast['SpendingForecast'])\n",
    "print(f'Spending VAR(5) RMSE: {RMSE2:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparing AR(5) model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.ar_model import AutoReg #AR#,ARResults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:\n",
      "const       0.467296\n",
      "Money.L1   -0.609867\n",
      "Money.L2   -0.470648\n",
      "Money.L3   -0.231296\n",
      "Money.L4   -0.315501\n",
      "Money.L5   -0.129869\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "modelM = AutoReg(train['Money'],lags=5)\n",
    "AR5fit1 = modelM.fit()\n",
    "#print(f'Lag: {AR5fit1.k_ar}')\n",
    "print(f'Coefficients:\\n{AR5fit1.params}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "z1 = pd.DataFrame(AR5fit1.predict(start=start, end=end, dynamic=False),columns=['Money'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>-17.119701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>-11.528475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>9.827790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>-5.836863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>2.339541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>5.375022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>-4.165296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>0.501917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>0.898255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>-1.352987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>1.369693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>0.443569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Money\n",
       "2015-01-01 -17.119701\n",
       "2015-02-01 -11.528475\n",
       "2015-03-01   9.827790\n",
       "2015-04-01  -5.836863\n",
       "2015-05-01   2.339541\n",
       "2015-06-01   5.375022\n",
       "2015-07-01  -4.165296\n",
       "2015-08-01   0.501917\n",
       "2015-09-01   0.898255\n",
       "2015-10-01  -1.352987\n",
       "2015-11-01   1.369693\n",
       "2015-12-01   0.443569"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### inverting the transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the most recent first difference from the training set to the forecast cumulative sum\n",
    "z1['Money1d'] = (df['Money'].iloc[-nobs-1]-df['Money'].iloc[-nobs-2]) + z1['Money'].cumsum()\n",
    "\n",
    "# Now build the forecast values from the first difference set\n",
    "z1['MoneyForecast'] = df['Money'].iloc[-nobs-1] + z1['Money1d'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money</th>\n",
       "      <th>Money1d</th>\n",
       "      <th>MoneyForecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>-17.119701</td>\n",
       "      <td>61.480299</td>\n",
       "      <td>11731.580299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>-11.528475</td>\n",
       "      <td>49.951824</td>\n",
       "      <td>11781.532123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>9.827790</td>\n",
       "      <td>59.779614</td>\n",
       "      <td>11841.311736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>-5.836863</td>\n",
       "      <td>53.942751</td>\n",
       "      <td>11895.254487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>2.339541</td>\n",
       "      <td>56.282292</td>\n",
       "      <td>11951.536779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>5.375022</td>\n",
       "      <td>61.657314</td>\n",
       "      <td>12013.194093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>-4.165296</td>\n",
       "      <td>57.492019</td>\n",
       "      <td>12070.686112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>0.501917</td>\n",
       "      <td>57.993936</td>\n",
       "      <td>12128.680048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>0.898255</td>\n",
       "      <td>58.892191</td>\n",
       "      <td>12187.572238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>-1.352987</td>\n",
       "      <td>57.539203</td>\n",
       "      <td>12245.111442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>1.369693</td>\n",
       "      <td>58.908896</td>\n",
       "      <td>12304.020338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>0.443569</td>\n",
       "      <td>59.352466</td>\n",
       "      <td>12363.372804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Money    Money1d  MoneyForecast\n",
       "2015-01-01 -17.119701  61.480299   11731.580299\n",
       "2015-02-01 -11.528475  49.951824   11781.532123\n",
       "2015-03-01   9.827790  59.779614   11841.311736\n",
       "2015-04-01  -5.836863  53.942751   11895.254487\n",
       "2015-05-01   2.339541  56.282292   11951.536779\n",
       "2015-06-01   5.375022  61.657314   12013.194093\n",
       "2015-07-01  -4.165296  57.492019   12070.686112\n",
       "2015-08-01   0.501917  57.993936   12128.680048\n",
       "2015-09-01   0.898255  58.892191   12187.572238\n",
       "2015-10-01  -1.352987  57.539203   12245.111442\n",
       "2015-11-01   1.369693  58.908896   12304.020338\n",
       "2015-12-01   0.443569  59.352466   12363.372804"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Money VAR(5) RMSE: 43.710\n",
      "Money  AR(5) RMSE: 34.394\n"
     ]
    }
   ],
   "source": [
    "RMSE3 = rmse(df['Money'][-nobs:], z1['MoneyForecast'])\n",
    "\n",
    "print(f'Money VAR(5) RMSE: {RMSE1:.3f}')\n",
    "print(f'Money  AR(5) RMSE: {RMSE3:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Personal Spending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:\n",
      "const       0.467296\n",
      "Money.L1   -0.609867\n",
      "Money.L2   -0.470648\n",
      "Money.L3   -0.231296\n",
      "Money.L4   -0.315501\n",
      "Money.L5   -0.129869\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "modelS = AutoReg(train['Spending'],lags=5)\n",
    "AR5fit2 = modelM.fit()\n",
    "#print(f'Lag: {AR5fit1.k_ar}')\n",
    "print(f'Coefficients:\\n{AR5fit1.params}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Spending</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>-17.119701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>-11.528475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>9.827790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>-5.836863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>2.339541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>5.375022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>-4.165296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>0.501917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>0.898255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>-1.352987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>1.369693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>0.443569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Spending\n",
       "2015-01-01 -17.119701\n",
       "2015-02-01 -11.528475\n",
       "2015-03-01   9.827790\n",
       "2015-04-01  -5.836863\n",
       "2015-05-01   2.339541\n",
       "2015-06-01   5.375022\n",
       "2015-07-01  -4.165296\n",
       "2015-08-01   0.501917\n",
       "2015-09-01   0.898255\n",
       "2015-10-01  -1.352987\n",
       "2015-11-01   1.369693\n",
       "2015-12-01   0.443569"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z2 = pd.DataFrame(AR5fit2.predict(start=start, end=end, dynamic=False),columns=['Spending'])\n",
    "z2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### invert the transformation and evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the most recent first difference from the training set to the forecast cumulative sum\n",
    "z2['Spending1d'] = (df['Spending'].iloc[-nobs-1]-df['Spending'].iloc[-nobs-2]) + z2['Spending'].cumsum()\n",
    "\n",
    "# Now build the forecast values from the first difference set\n",
    "z2['SpendingForecast'] = df['Spending'].iloc[-nobs-1] + z2['Spending1d'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Spending</th>\n",
       "      <th>Spending1d</th>\n",
       "      <th>SpendingForecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>-17.119701</td>\n",
       "      <td>-6.519701</td>\n",
       "      <td>12055.480299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>-11.528475</td>\n",
       "      <td>-18.048176</td>\n",
       "      <td>12037.432123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>9.827790</td>\n",
       "      <td>-8.220386</td>\n",
       "      <td>12029.211736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>-5.836863</td>\n",
       "      <td>-14.057249</td>\n",
       "      <td>12015.154487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>2.339541</td>\n",
       "      <td>-11.717708</td>\n",
       "      <td>12003.436779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>5.375022</td>\n",
       "      <td>-6.342686</td>\n",
       "      <td>11997.094093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>-4.165296</td>\n",
       "      <td>-10.507981</td>\n",
       "      <td>11986.586112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>0.501917</td>\n",
       "      <td>-10.006064</td>\n",
       "      <td>11976.580048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>0.898255</td>\n",
       "      <td>-9.107809</td>\n",
       "      <td>11967.472238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>-1.352987</td>\n",
       "      <td>-10.460797</td>\n",
       "      <td>11957.011442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>1.369693</td>\n",
       "      <td>-9.091104</td>\n",
       "      <td>11947.920338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>0.443569</td>\n",
       "      <td>-8.647534</td>\n",
       "      <td>11939.272804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Spending  Spending1d  SpendingForecast\n",
       "2015-01-01 -17.119701   -6.519701      12055.480299\n",
       "2015-02-01 -11.528475  -18.048176      12037.432123\n",
       "2015-03-01   9.827790   -8.220386      12029.211736\n",
       "2015-04-01  -5.836863  -14.057249      12015.154487\n",
       "2015-05-01   2.339541  -11.717708      12003.436779\n",
       "2015-06-01   5.375022   -6.342686      11997.094093\n",
       "2015-07-01  -4.165296  -10.507981      11986.586112\n",
       "2015-08-01   0.501917  -10.006064      11976.580048\n",
       "2015-09-01   0.898255   -9.107809      11967.472238\n",
       "2015-10-01  -1.352987  -10.460797      11957.011442\n",
       "2015-11-01   1.369693   -9.091104      11947.920338\n",
       "2015-12-01   0.443569   -8.647534      11939.272804"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spending VAR(5) RMSE: 37.001\n",
      "Spending  AR(5) RMSE: 347.900\n"
     ]
    }
   ],
   "source": [
    "RMSE4 = rmse(df['Spending'][-nobs:], z2['SpendingForecast'])\n",
    "\n",
    "print(f'Spending VAR(5) RMSE: {RMSE2:.3f}')\n",
    "print(f'Spending  AR(5) RMSE: {RMSE4:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AR(5) model looks better than the VAR(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VARMA MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "# Load specific forecasting tools\n",
    "\n",
    "from statsmodels.tsa.api import VARMAX#, VARMAXResults#, DynamicVAR\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tools.eval_measures import rmse\n",
    "\n",
    "# Ignore harmless warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Load datasets\n",
    "df = pd.read_csv('./UDEMY_TSA_FINAL/Data/M2SLMoneyStock.csv',index_col=0, parse_dates=True)\n",
    "df.index.freq = 'MS'\n",
    "\n",
    "sp = pd.read_csv('./UDEMY_TSA_FINAL/Data/PCEPersonalSpending.csv',index_col=0, parse_dates=True)\n",
    "sp.index.freq = 'MS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money</th>\n",
       "      <th>Spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>3492.4</td>\n",
       "      <td>4851.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-02-01</th>\n",
       "      <td>3489.9</td>\n",
       "      <td>4850.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-03-01</th>\n",
       "      <td>3491.1</td>\n",
       "      <td>4885.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-04-01</th>\n",
       "      <td>3499.2</td>\n",
       "      <td>4890.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-01</th>\n",
       "      <td>3524.2</td>\n",
       "      <td>4933.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Money  Spending\n",
       "Date                        \n",
       "1995-01-01  3492.4    4851.2\n",
       "1995-02-01  3489.9    4850.8\n",
       "1995-03-01  3491.1    4885.4\n",
       "1995-04-01  3499.2    4890.2\n",
       "1995-05-01  3524.2    4933.1"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.join(sp)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(252, 2)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'M2 Money Stock vs Peronal Consumption Expend'}, xlabel='Date', ylabel='Billions of dollars'>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(ylabel=\"Billions of dollars\",title=\"M2 Money Stock vs Peronal Consumption Expend\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### stationary check and transfomations to make stationary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# INCLUDED HERE IF YOU CHOOSE TO USE IT\n",
    "def adf_test(series,title=''):\n",
    "    \"\"\"\n",
    "    Pass in a time series and an optional title, returns an ADF report\n",
    "    \"\"\"\n",
    "    print(f'Augmented Dickey-Fuller Test: {title}')\n",
    "    result = adfuller(series.dropna(),autolag='AIC') # .dropna() handles differenced data\n",
    "    \n",
    "    labels = ['ADF test statistic','p-value','# lags used','# observations']\n",
    "    out = pd.Series(result[0:4],index=labels)\n",
    "\n",
    "    for key,val in result[4].items():\n",
    "        out[f'critical value ({key})']=val\n",
    "        \n",
    "    print(out.to_string())          # .to_string() removes the line \"dtype: float64\"\n",
    "    \n",
    "    if result[1] <= 0.05:\n",
    "        print(\"Strong evidence against the null hypothesis\")\n",
    "        print(\"Reject the null hypothesis\")\n",
    "        print(\"Data has no unit root and is stationary\")\n",
    "    else:\n",
    "        print(\"Weak evidence against the null hypothesis\")\n",
    "        print(\"Fail to reject the null hypothesis\")\n",
    "        print(\"Data has a unit root and is non-stationary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ARIMA(maxiter=1000, order=(1, 2, 1), scoring_args={}, suppress_warnings=True,\n",
       "      with_intercept=False)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pmdarima import auto_arima\n",
    "\n",
    "auto_arima(df['Money'],maxiter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ARIMA(maxiter=1000, order=(1, 1, 2), scoring_args={}, suppress_warnings=True)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_arima(df['Spending'],maxiter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money</th>\n",
       "      <th>Spending</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-03-01</th>\n",
       "      <td>3.7</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-04-01</th>\n",
       "      <td>6.9</td>\n",
       "      <td>-29.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-01</th>\n",
       "      <td>16.9</td>\n",
       "      <td>38.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-06-01</th>\n",
       "      <td>-0.3</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-07-01</th>\n",
       "      <td>-6.2</td>\n",
       "      <td>-51.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Money  Spending\n",
       "Date                       \n",
       "1995-03-01    3.7      35.0\n",
       "1995-04-01    6.9     -29.8\n",
       "1995-05-01   16.9      38.1\n",
       "1995-06-01   -0.3       1.5\n",
       "1995-07-01   -6.2     -51.7"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed=df.diff().diff()\n",
    "df_transformed=df_transformed.dropna()\n",
    "df_transformed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_transformed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "nobs=12\n",
    "train, test=df_transformed[0:-nobs], df_transformed[-nobs:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((238, 2), (12, 2))"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting VARMA 1,2 Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Statespace Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>['Money', 'Spending']</td> <th>  No. Observations:  </th>    <td>238</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                <td>VARMA(1,2)</td>       <th>  Log Likelihood     </th> <td>-2286.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td>+ intercept</td>      <th>  AIC                </th> <td>4606.136</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Thu, 20 Jul 2023</td>    <th>  BIC                </th> <td>4665.165</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>21:26:21</td>        <th>  HQIC               </th> <td>4629.926</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>               <td>03-01-1995</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                     <td>- 12-01-2014</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>          <td>opg</td>          <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.39, 0.00</td> <th>  Jarque-Bera (JB):  </th> <td>559.96, 125.81</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.53, 0.95</td> <th>  Prob(JB):          </th>   <td>0.00, 0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>5.64, 2.90</td> <th>  Skew:              </th>   <td>1.34, -0.35</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00, 0.00</td> <th>  Kurtosis:          </th>   <td>10.02, 6.49</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Results for equation Money</caption>\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>      <td>    0.0359</td> <td>    0.866</td> <td>    0.041</td> <td> 0.967</td> <td>   -1.662</td> <td>    1.734</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.Money</th>       <td>   -1.2886</td> <td>    5.055</td> <td>   -0.255</td> <td> 0.799</td> <td>  -11.196</td> <td>    8.619</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.Spending</th>    <td>    2.2806</td> <td>    8.362</td> <td>    0.273</td> <td> 0.785</td> <td>  -14.109</td> <td>   18.670</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(Money)</th>    <td>    0.5198</td> <td>    5.045</td> <td>    0.103</td> <td> 0.918</td> <td>   -9.369</td> <td>   10.409</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(Spending)</th> <td>   -2.4054</td> <td>    8.365</td> <td>   -0.288</td> <td> 0.774</td> <td>  -18.800</td> <td>   13.989</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(Money)</th>    <td>   -1.5210</td> <td>    5.040</td> <td>   -0.302</td> <td> 0.763</td> <td>  -11.399</td> <td>    8.357</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(Spending)</th> <td>    2.1075</td> <td>    7.348</td> <td>    0.287</td> <td> 0.774</td> <td>  -12.295</td> <td>   16.510</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Results for equation Spending</caption>\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>      <td>    0.0933</td> <td>    0.249</td> <td>    0.374</td> <td> 0.708</td> <td>   -0.395</td> <td>    0.582</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.Money</th>       <td>   -0.4399</td> <td>    3.189</td> <td>   -0.138</td> <td> 0.890</td> <td>   -6.689</td> <td>    5.810</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.Spending</th>    <td>    0.8698</td> <td>    5.070</td> <td>    0.172</td> <td> 0.864</td> <td>   -9.067</td> <td>   10.807</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(Money)</th>    <td>    0.5633</td> <td>    3.244</td> <td>    0.174</td> <td> 0.862</td> <td>   -5.795</td> <td>    6.922</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(Spending)</th> <td>   -1.8290</td> <td>    5.050</td> <td>   -0.362</td> <td> 0.717</td> <td>  -11.726</td> <td>    8.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(Money)</th>    <td>   -0.5637</td> <td>    3.249</td> <td>   -0.173</td> <td> 0.862</td> <td>   -6.931</td> <td>    5.804</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(Spending)</th> <td>    0.8383</td> <td>    4.415</td> <td>    0.190</td> <td> 0.849</td> <td>   -7.815</td> <td>    9.492</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Error covariance matrix</caption>\n",
       "<tr>\n",
       "             <td></td>                <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.var.Money</th>          <td>   25.5580</td> <td>    2.474</td> <td>   10.331</td> <td> 0.000</td> <td>   20.709</td> <td>   30.407</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.Money.Spending</th> <td>  -10.1069</td> <td>    2.066</td> <td>   -4.891</td> <td> 0.000</td> <td>  -14.157</td> <td>   -6.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.var.Spending</th>       <td>   33.4131</td> <td>    1.265</td> <td>   26.418</td> <td> 0.000</td> <td>   30.934</td> <td>   35.892</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             Statespace Model Results                            \n",
       "=================================================================================\n",
       "Dep. Variable:     ['Money', 'Spending']   No. Observations:                  238\n",
       "Model:                        VARMA(1,2)   Log Likelihood               -2286.068\n",
       "                             + intercept   AIC                           4606.136\n",
       "Date:                   Thu, 20 Jul 2023   BIC                           4665.165\n",
       "Time:                           21:26:21   HQIC                          4629.926\n",
       "Sample:                       03-01-1995                                         \n",
       "                            - 12-01-2014                                         \n",
       "Covariance Type:                     opg                                         \n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):             0.39, 0.00   Jarque-Bera (JB):       559.96, 125.81\n",
       "Prob(Q):                        0.53, 0.95   Prob(JB):                   0.00, 0.00\n",
       "Heteroskedasticity (H):         5.64, 2.90   Skew:                      1.34, -0.35\n",
       "Prob(H) (two-sided):            0.00, 0.00   Kurtosis:                  10.02, 6.49\n",
       "                            Results for equation Money                            \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "intercept          0.0359      0.866      0.041      0.967      -1.662       1.734\n",
       "L1.Money          -1.2886      5.055     -0.255      0.799     -11.196       8.619\n",
       "L1.Spending        2.2806      8.362      0.273      0.785     -14.109      18.670\n",
       "L1.e(Money)        0.5198      5.045      0.103      0.918      -9.369      10.409\n",
       "L1.e(Spending)    -2.4054      8.365     -0.288      0.774     -18.800      13.989\n",
       "L2.e(Money)       -1.5210      5.040     -0.302      0.763     -11.399       8.357\n",
       "L2.e(Spending)     2.1075      7.348      0.287      0.774     -12.295      16.510\n",
       "                          Results for equation Spending                           \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "intercept          0.0933      0.249      0.374      0.708      -0.395       0.582\n",
       "L1.Money          -0.4399      3.189     -0.138      0.890      -6.689       5.810\n",
       "L1.Spending        0.8698      5.070      0.172      0.864      -9.067      10.807\n",
       "L1.e(Money)        0.5633      3.244      0.174      0.862      -5.795       6.922\n",
       "L1.e(Spending)    -1.8290      5.050     -0.362      0.717     -11.726       8.068\n",
       "L2.e(Money)       -0.5637      3.249     -0.173      0.862      -6.931       5.804\n",
       "L2.e(Spending)     0.8383      4.415      0.190      0.849      -7.815       9.492\n",
       "                                  Error covariance matrix                                  \n",
       "===========================================================================================\n",
       "                              coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------------\n",
       "sqrt.var.Money             25.5580      2.474     10.331      0.000      20.709      30.407\n",
       "sqrt.cov.Money.Spending   -10.1069      2.066     -4.891      0.000     -14.157      -6.057\n",
       "sqrt.var.Spending          33.4131      1.265     26.418      0.000      30.934      35.892\n",
       "===========================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=VARMAX(train, order=(1,2),trend=\"c\")\n",
    "results=model.fit(maxiter=1000,disp=False)\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Forecasting next 12 values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money</th>\n",
       "      <th>Spending</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>-12.694635</td>\n",
       "      <td>38.344362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>-10.927281</td>\n",
       "      <td>-5.540071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>1.482099</td>\n",
       "      <td>0.080905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>-1.689457</td>\n",
       "      <td>-0.488199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>1.099531</td>\n",
       "      <td>0.411824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>-0.441778</td>\n",
       "      <td>-0.032076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>0.532000</td>\n",
       "      <td>0.259768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>-0.057235</td>\n",
       "      <td>0.085296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>0.304154</td>\n",
       "      <td>0.192716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>0.083450</td>\n",
       "      <td>0.127192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>0.218417</td>\n",
       "      <td>0.167277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>0.135915</td>\n",
       "      <td>0.142777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Money   Spending\n",
       "2015-01-01 -12.694635  38.344362\n",
       "2015-02-01 -10.927281  -5.540071\n",
       "2015-03-01   1.482099   0.080905\n",
       "2015-04-01  -1.689457  -0.488199\n",
       "2015-05-01   1.099531   0.411824\n",
       "2015-06-01  -0.441778  -0.032076\n",
       "2015-07-01   0.532000   0.259768\n",
       "2015-08-01  -0.057235   0.085296\n",
       "2015-09-01   0.304154   0.192716\n",
       "2015-10-01   0.083450   0.127192\n",
       "2015-11-01   0.218417   0.167277\n",
       "2015-12-01   0.135915   0.142777"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_forecast=results.forecast(12)\n",
    "df_forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### inverting the transformation back to original vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the most recent first difference from the training side of the original dataset to the forecast cumulative sum\n",
    "df_forecast['Money1d'] = (df['Money'].iloc[-nobs-1]-df['Money'].iloc[-nobs-2]) + df_forecast['Money'].cumsum()\n",
    "\n",
    "# Now build the forecast values from the first difference set\n",
    "df_forecast['MoneyForecast'] = df['Money'].iloc[-nobs-1] + df_forecast['Money'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the most recent first difference from the training side of the original dataset to the forecast cumulative sum\n",
    "df_forecast['Spending1d'] = (df['Spending'].iloc[-nobs-1]-df['Spending'].iloc[-nobs-2]) + df_forecast['Spending'].cumsum()\n",
    "\n",
    "# Now build the forecast values from the first difference set\n",
    "df_forecast['SpendingForecast'] = df['Spending'].iloc[-nobs-1] + df_forecast['Spending'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money</th>\n",
       "      <th>Spending</th>\n",
       "      <th>Money1d</th>\n",
       "      <th>MoneyForecast</th>\n",
       "      <th>Spending1d</th>\n",
       "      <th>SpendingForecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>-12.694635</td>\n",
       "      <td>38.344362</td>\n",
       "      <td>65.905365</td>\n",
       "      <td>11657.405365</td>\n",
       "      <td>48.944362</td>\n",
       "      <td>12100.344362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>-10.927281</td>\n",
       "      <td>-5.540071</td>\n",
       "      <td>54.978083</td>\n",
       "      <td>11646.478083</td>\n",
       "      <td>43.404291</td>\n",
       "      <td>12094.804291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>1.482099</td>\n",
       "      <td>0.080905</td>\n",
       "      <td>56.460182</td>\n",
       "      <td>11647.960182</td>\n",
       "      <td>43.485195</td>\n",
       "      <td>12094.885195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>-1.689457</td>\n",
       "      <td>-0.488199</td>\n",
       "      <td>54.770725</td>\n",
       "      <td>11646.270725</td>\n",
       "      <td>42.996997</td>\n",
       "      <td>12094.396997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>1.099531</td>\n",
       "      <td>0.411824</td>\n",
       "      <td>55.870255</td>\n",
       "      <td>11647.370255</td>\n",
       "      <td>43.408821</td>\n",
       "      <td>12094.808821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>-0.441778</td>\n",
       "      <td>-0.032076</td>\n",
       "      <td>55.428478</td>\n",
       "      <td>11646.928478</td>\n",
       "      <td>43.376745</td>\n",
       "      <td>12094.776745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>0.532000</td>\n",
       "      <td>0.259768</td>\n",
       "      <td>55.960477</td>\n",
       "      <td>11647.460477</td>\n",
       "      <td>43.636513</td>\n",
       "      <td>12095.036513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>-0.057235</td>\n",
       "      <td>0.085296</td>\n",
       "      <td>55.903243</td>\n",
       "      <td>11647.403243</td>\n",
       "      <td>43.721809</td>\n",
       "      <td>12095.121809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>0.304154</td>\n",
       "      <td>0.192716</td>\n",
       "      <td>56.207396</td>\n",
       "      <td>11647.707396</td>\n",
       "      <td>43.914524</td>\n",
       "      <td>12095.314524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>0.083450</td>\n",
       "      <td>0.127192</td>\n",
       "      <td>56.290846</td>\n",
       "      <td>11647.790846</td>\n",
       "      <td>44.041717</td>\n",
       "      <td>12095.441717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>0.218417</td>\n",
       "      <td>0.167277</td>\n",
       "      <td>56.509263</td>\n",
       "      <td>11648.009263</td>\n",
       "      <td>44.208994</td>\n",
       "      <td>12095.608994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>0.135915</td>\n",
       "      <td>0.142777</td>\n",
       "      <td>56.645178</td>\n",
       "      <td>11648.145178</td>\n",
       "      <td>44.351771</td>\n",
       "      <td>12095.751771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Money   Spending    Money1d  MoneyForecast  Spending1d  \\\n",
       "2015-01-01 -12.694635  38.344362  65.905365   11657.405365   48.944362   \n",
       "2015-02-01 -10.927281  -5.540071  54.978083   11646.478083   43.404291   \n",
       "2015-03-01   1.482099   0.080905  56.460182   11647.960182   43.485195   \n",
       "2015-04-01  -1.689457  -0.488199  54.770725   11646.270725   42.996997   \n",
       "2015-05-01   1.099531   0.411824  55.870255   11647.370255   43.408821   \n",
       "2015-06-01  -0.441778  -0.032076  55.428478   11646.928478   43.376745   \n",
       "2015-07-01   0.532000   0.259768  55.960477   11647.460477   43.636513   \n",
       "2015-08-01  -0.057235   0.085296  55.903243   11647.403243   43.721809   \n",
       "2015-09-01   0.304154   0.192716  56.207396   11647.707396   43.914524   \n",
       "2015-10-01   0.083450   0.127192  56.290846   11647.790846   44.041717   \n",
       "2015-11-01   0.218417   0.167277  56.509263   11648.009263   44.208994   \n",
       "2015-12-01   0.135915   0.142777  56.645178   11648.145178   44.351771   \n",
       "\n",
       "            SpendingForecast  \n",
       "2015-01-01      12100.344362  \n",
       "2015-02-01      12094.804291  \n",
       "2015-03-01      12094.885195  \n",
       "2015-04-01      12094.396997  \n",
       "2015-05-01      12094.808821  \n",
       "2015-06-01      12094.776745  \n",
       "2015-07-01      12095.036513  \n",
       "2015-08-01      12095.121809  \n",
       "2015-09-01      12095.314524  \n",
       "2015-10-01      12095.441717  \n",
       "2015-11-01      12095.608994  \n",
       "2015-12-01      12095.751771  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money</th>\n",
       "      <th>Spending</th>\n",
       "      <th>MoneyForecast</th>\n",
       "      <th>SpendingForecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>11733.2</td>\n",
       "      <td>12046.0</td>\n",
       "      <td>11657.405365</td>\n",
       "      <td>12100.344362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>11852.4</td>\n",
       "      <td>12082.4</td>\n",
       "      <td>11646.478083</td>\n",
       "      <td>12094.804291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>11868.8</td>\n",
       "      <td>12158.3</td>\n",
       "      <td>11647.960182</td>\n",
       "      <td>12094.885195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>11916.1</td>\n",
       "      <td>12193.8</td>\n",
       "      <td>11646.270725</td>\n",
       "      <td>12094.396997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>11947.6</td>\n",
       "      <td>12268.1</td>\n",
       "      <td>11647.370255</td>\n",
       "      <td>12094.808821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>11993.1</td>\n",
       "      <td>12308.3</td>\n",
       "      <td>11646.928478</td>\n",
       "      <td>12094.776745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>12045.3</td>\n",
       "      <td>12355.4</td>\n",
       "      <td>11647.460477</td>\n",
       "      <td>12095.036513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>12096.8</td>\n",
       "      <td>12394.0</td>\n",
       "      <td>11647.403243</td>\n",
       "      <td>12095.121809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>12153.8</td>\n",
       "      <td>12392.8</td>\n",
       "      <td>11647.707396</td>\n",
       "      <td>12095.314524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>12187.7</td>\n",
       "      <td>12416.1</td>\n",
       "      <td>11647.790846</td>\n",
       "      <td>12095.441717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>12277.4</td>\n",
       "      <td>12450.1</td>\n",
       "      <td>11648.009263</td>\n",
       "      <td>12095.608994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>12335.9</td>\n",
       "      <td>12469.1</td>\n",
       "      <td>11648.145178</td>\n",
       "      <td>12095.751771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Money  Spending  MoneyForecast  SpendingForecast\n",
       "2015-01-01  11733.2   12046.0   11657.405365      12100.344362\n",
       "2015-02-01  11852.4   12082.4   11646.478083      12094.804291\n",
       "2015-03-01  11868.8   12158.3   11647.960182      12094.885195\n",
       "2015-04-01  11916.1   12193.8   11646.270725      12094.396997\n",
       "2015-05-01  11947.6   12268.1   11647.370255      12094.808821\n",
       "2015-06-01  11993.1   12308.3   11646.928478      12094.776745\n",
       "2015-07-01  12045.3   12355.4   11647.460477      12095.036513\n",
       "2015-08-01  12096.8   12394.0   11647.403243      12095.121809\n",
       "2015-09-01  12153.8   12392.8   11647.707396      12095.314524\n",
       "2015-10-01  12187.7   12416.1   11647.790846      12095.441717\n",
       "2015-11-01  12277.4   12450.1   11648.009263      12095.608994\n",
       "2015-12-01  12335.9   12469.1   11648.145178      12095.751771"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df.iloc[-12:],df_forecast[['MoneyForecast','SpendingForecast']]],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Money'][-nobs:].plot(figsize=(12,5),legend=True).autoscale(axis='x',tight=True)\n",
    "df_forecast['MoneyForecast'].plot(legend=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Spending'][-nobs:].plot(figsize=(12,5),legend=True).autoscale(axis='x',tight=True)\n",
    "df_forecast['SpendingForecast'].plot(legend=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Money VAR(5) RMSE: 423.940\n",
      "Spending VAR(5) RMSE: 242.577\n"
     ]
    }
   ],
   "source": [
    "RMSE1 = rmse(df['Money'][-nobs:], df_forecast['MoneyForecast'])\n",
    "print(f'Money VAR(5) RMSE: {RMSE1:.3f}')\n",
    "\n",
    "RMSE2 = rmse(df['Spending'][-nobs:], df_forecast['SpendingForecast'])\n",
    "print(f'Spending VAR(5) RMSE: {RMSE2:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare with ARMA(1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Money</td>      <th>  No. Observations:  </th>    <td>238</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(1, 0, 2)</td>  <th>  Log Likelihood     </th> <td>-1117.710</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 20 Jul 2023</td> <th>  AIC                </th> <td>2245.421</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>21:31:41</td>     <th>  BIC                </th> <td>2262.782</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>03-01-1995</td>    <th>  HQIC               </th> <td>2252.418</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 12-01-2014</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    0.1814</td> <td>    0.032</td> <td>    5.639</td> <td> 0.000</td> <td>    0.118</td> <td>    0.244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>   -0.3568</td> <td>    0.176</td> <td>   -2.029</td> <td> 0.042</td> <td>   -0.701</td> <td>   -0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.4087</td> <td>    1.937</td> <td>   -0.211</td> <td> 0.833</td> <td>   -4.205</td> <td>    3.388</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>  <td>   -0.5911</td> <td>    1.102</td> <td>   -0.537</td> <td> 0.592</td> <td>   -2.750</td> <td>    1.568</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>  687.2002</td> <td> 1307.788</td> <td>    0.525</td> <td> 0.599</td> <td>-1876.017</td> <td> 3250.417</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.34</td> <th>  Jarque-Bera (JB):  </th> <td>775.34</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.56</td> <th>  Prob(JB):          </th>  <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>6.83</td> <th>  Skew:              </th>  <td>1.61</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>11.23</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                  Money   No. Observations:                  238\n",
       "Model:                 ARIMA(1, 0, 2)   Log Likelihood               -1117.710\n",
       "Date:                Thu, 20 Jul 2023   AIC                           2245.421\n",
       "Time:                        21:31:41   BIC                           2262.782\n",
       "Sample:                    03-01-1995   HQIC                          2252.418\n",
       "                         - 12-01-2014                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.1814      0.032      5.639      0.000       0.118       0.244\n",
       "ar.L1         -0.3568      0.176     -2.029      0.042      -0.701      -0.012\n",
       "ma.L1         -0.4087      1.937     -0.211      0.833      -4.205       3.388\n",
       "ma.L2         -0.5911      1.102     -0.537      0.592      -2.750       1.568\n",
       "sigma2       687.2002   1307.788      0.525      0.599   -1876.017    3250.417\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.34   Jarque-Bera (JB):               775.34\n",
       "Prob(Q):                              0.56   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               6.83   Skew:                             1.61\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        11.23\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ARIMA(train['Money'],order=(1,0,2))\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "z1 = results.predict(start=start, end=end).rename('Money')\n",
    "z1 = pd.DataFrame(z1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>-14.429077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>-10.904154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>4.136412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>-1.229654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>0.684813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>0.001783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>0.245470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>0.158529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>0.189547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>0.178481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>0.182429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>0.181020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Money\n",
       "2015-01-01 -14.429077\n",
       "2015-02-01 -10.904154\n",
       "2015-03-01   4.136412\n",
       "2015-04-01  -1.229654\n",
       "2015-05-01   0.684813\n",
       "2015-06-01   0.001783\n",
       "2015-07-01   0.245470\n",
       "2015-08-01   0.158529\n",
       "2015-09-01   0.189547\n",
       "2015-10-01   0.178481\n",
       "2015-11-01   0.182429\n",
       "2015-12-01   0.181020"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### imvert and evaluate the forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the most recent first difference from the training set to the forecast cumulative sum\n",
    "z1['Money1d'] = (df['Money'].iloc[-nobs-1]-df['Money'].iloc[-nobs-2]) + z1['Money'].cumsum()\n",
    "\n",
    "# Now build the forecast values from the first difference set\n",
    "z1['MoneyForecast'] = df['Money'].iloc[-nobs-1] + z1['Money1d'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Money</th>\n",
       "      <th>Money1d</th>\n",
       "      <th>MoneyForecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>-14.429077</td>\n",
       "      <td>64.170923</td>\n",
       "      <td>11734.270923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>-10.904154</td>\n",
       "      <td>53.266769</td>\n",
       "      <td>11787.537692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>4.136412</td>\n",
       "      <td>57.403182</td>\n",
       "      <td>11844.940874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>-1.229654</td>\n",
       "      <td>56.173527</td>\n",
       "      <td>11901.114401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>0.684813</td>\n",
       "      <td>56.858341</td>\n",
       "      <td>11957.972742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>0.001783</td>\n",
       "      <td>56.860124</td>\n",
       "      <td>12014.832865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>0.245470</td>\n",
       "      <td>57.105593</td>\n",
       "      <td>12071.938459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>0.158529</td>\n",
       "      <td>57.264122</td>\n",
       "      <td>12129.202581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>0.189547</td>\n",
       "      <td>57.453669</td>\n",
       "      <td>12186.656250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>0.178481</td>\n",
       "      <td>57.632150</td>\n",
       "      <td>12244.288400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>0.182429</td>\n",
       "      <td>57.814578</td>\n",
       "      <td>12302.102978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>0.181020</td>\n",
       "      <td>57.995599</td>\n",
       "      <td>12360.098577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Money    Money1d  MoneyForecast\n",
       "2015-01-01 -14.429077  64.170923   11734.270923\n",
       "2015-02-01 -10.904154  53.266769   11787.537692\n",
       "2015-03-01   4.136412  57.403182   11844.940874\n",
       "2015-04-01  -1.229654  56.173527   11901.114401\n",
       "2015-05-01   0.684813  56.858341   11957.972742\n",
       "2015-06-01   0.001783  56.860124   12014.832865\n",
       "2015-07-01   0.245470  57.105593   12071.938459\n",
       "2015-08-01   0.158529  57.264122   12129.202581\n",
       "2015-09-01   0.189547  57.453669   12186.656250\n",
       "2015-10-01   0.178481  57.632150   12244.288400\n",
       "2015-11-01   0.182429  57.814578   12302.102978\n",
       "2015-12-01   0.181020  57.995599   12360.098577"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Money VARMA(1,2) RMSE: 423.940\n",
      "Money  ARMA(1,2) RMSE: 32.684\n"
     ]
    }
   ],
   "source": [
    "RMSE3 = rmse(df['Money'][-nobs:], z1['MoneyForecast'])\n",
    "\n",
    "print(f'Money VARMA(1,2) RMSE: {RMSE1:.3f}')\n",
    "print(f'Money  ARMA(1,2) RMSE: {RMSE3:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Personal Spending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Spending</td>     <th>  No. Observations:  </th>    <td>238</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(1, 0, 2)</td>  <th>  Log Likelihood     </th> <td>-1182.411</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 20 Jul 2023</td> <th>  AIC                </th> <td>2374.823</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>21:32:43</td>     <th>  BIC                </th> <td>2392.184</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>03-01-1995</td>    <th>  HQIC               </th> <td>2381.820</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 12-01-2014</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    0.0856</td> <td>    0.279</td> <td>    0.307</td> <td> 0.759</td> <td>   -0.461</td> <td>    0.632</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>   -0.3403</td> <td>    0.545</td> <td>   -0.624</td> <td> 0.532</td> <td>   -1.408</td> <td>    0.728</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.6451</td> <td>    0.549</td> <td>   -1.174</td> <td> 0.240</td> <td>   -1.722</td> <td>    0.432</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>  <td>   -0.2139</td> <td>    0.507</td> <td>   -0.422</td> <td> 0.673</td> <td>   -1.207</td> <td>    0.779</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td> 1201.3966</td> <td>   66.509</td> <td>   18.064</td> <td> 0.000</td> <td> 1071.042</td> <td> 1331.751</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>184.55</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.99</td> <th>  Prob(JB):          </th>  <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>2.72</td> <th>  Skew:              </th>  <td>-0.17</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>7.30</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:               Spending   No. Observations:                  238\n",
       "Model:                 ARIMA(1, 0, 2)   Log Likelihood               -1182.411\n",
       "Date:                Thu, 20 Jul 2023   AIC                           2374.823\n",
       "Time:                        21:32:43   BIC                           2392.184\n",
       "Sample:                    03-01-1995   HQIC                          2381.820\n",
       "                         - 12-01-2014                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.0856      0.279      0.307      0.759      -0.461       0.632\n",
       "ar.L1         -0.3403      0.545     -0.624      0.532      -1.408       0.728\n",
       "ma.L1         -0.6451      0.549     -1.174      0.240      -1.722       0.432\n",
       "ma.L2         -0.2139      0.507     -0.422      0.673      -1.207       0.779\n",
       "sigma2      1201.3966     66.509     18.064      0.000    1071.042    1331.751\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):               184.55\n",
       "Prob(Q):                              0.99   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               2.72   Skew:                            -0.17\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         7.30\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ARIMA(train['Spending'],order=(1,0,2))\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Spending</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>33.555507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>-3.338121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>1.250569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>-0.310791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>0.220481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>0.039709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>0.101219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>0.080290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>0.087411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>0.084988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>0.085813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>0.085532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Spending\n",
       "2015-01-01  33.555507\n",
       "2015-02-01  -3.338121\n",
       "2015-03-01   1.250569\n",
       "2015-04-01  -0.310791\n",
       "2015-05-01   0.220481\n",
       "2015-06-01   0.039709\n",
       "2015-07-01   0.101219\n",
       "2015-08-01   0.080290\n",
       "2015-09-01   0.087411\n",
       "2015-10-01   0.084988\n",
       "2015-11-01   0.085813\n",
       "2015-12-01   0.085532"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "z2 = results.predict(start=start, end=end).rename('Spending')\n",
    "z2 = pd.DataFrame(z2)\n",
    "z2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the most recent first difference from the training set to the forecast cumulative sum\n",
    "z2['Spending1d'] = (df['Spending'].iloc[-nobs-1]-df['Spending'].iloc[-nobs-2]) + z2['Spending'].cumsum()\n",
    "\n",
    "# Now build the forecast values from the first difference set\n",
    "z2['SpendingForecast'] = df['Spending'].iloc[-nobs-1] + z2['Spending1d'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Spending</th>\n",
       "      <th>Spending1d</th>\n",
       "      <th>SpendingForecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>33.555507</td>\n",
       "      <td>44.155507</td>\n",
       "      <td>12106.155507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>-3.338121</td>\n",
       "      <td>40.817387</td>\n",
       "      <td>12146.972894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>1.250569</td>\n",
       "      <td>42.067955</td>\n",
       "      <td>12189.040850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>-0.310791</td>\n",
       "      <td>41.757165</td>\n",
       "      <td>12230.798014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>0.220481</td>\n",
       "      <td>41.977646</td>\n",
       "      <td>12272.775660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>0.039709</td>\n",
       "      <td>42.017355</td>\n",
       "      <td>12314.793015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>0.101219</td>\n",
       "      <td>42.118575</td>\n",
       "      <td>12356.911590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>0.080290</td>\n",
       "      <td>42.198864</td>\n",
       "      <td>12399.110454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>0.087411</td>\n",
       "      <td>42.286276</td>\n",
       "      <td>12441.396730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>0.084988</td>\n",
       "      <td>42.371264</td>\n",
       "      <td>12483.767994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>0.085813</td>\n",
       "      <td>42.457076</td>\n",
       "      <td>12526.225070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>0.085532</td>\n",
       "      <td>42.542608</td>\n",
       "      <td>12568.767678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Spending  Spending1d  SpendingForecast\n",
       "2015-01-01  33.555507   44.155507      12106.155507\n",
       "2015-02-01  -3.338121   40.817387      12146.972894\n",
       "2015-03-01   1.250569   42.067955      12189.040850\n",
       "2015-04-01  -0.310791   41.757165      12230.798014\n",
       "2015-05-01   0.220481   41.977646      12272.775660\n",
       "2015-06-01   0.039709   42.017355      12314.793015\n",
       "2015-07-01   0.101219   42.118575      12356.911590\n",
       "2015-08-01   0.080290   42.198864      12399.110454\n",
       "2015-09-01   0.087411   42.286276      12441.396730\n",
       "2015-10-01   0.084988   42.371264      12483.767994\n",
       "2015-11-01   0.085813   42.457076      12526.225070\n",
       "2015-12-01   0.085532   42.542608      12568.767678"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spending VARMA(1,2) RMSE: 242.577\n",
      "Spending  ARMA(1,2) RMSE: 52.332\n"
     ]
    }
   ],
   "source": [
    "RMSE4 = rmse(df['Spending'][-nobs:], z2['SpendingForecast'])\n",
    "\n",
    "print(f'Spending VARMA(1,2) RMSE: {RMSE2:.3f}')\n",
    "print(f'Spending  ARMA(1,2) RMSE: {RMSE4:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### VARMA does poor job than ARMA model"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}