VAR (vector autoregression) models .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VAR (vector autoregression) models\n",
    "- **In SARIMAX models forecast variable influenced by exogenous(input) variable but not vice-versa**\n",
    "- **When VAR models are great if forecast variable influenced by exogenous(input) variable and vice-versa**\n",
    "- General steps for VAR models:\n",
    " - examine the data\n",
    " - visualize the data\n",
    " - test for stationarity\n",
    " - build a model on training data\n",
    " - predict on test data\n",
    " - forecast for the future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('data/M2SLMoneyStock.csv', index_col= 0, parse_dates= True)\n",
    "df2 = pd.read_csv('data/PCEPersonalSpending.csv', index_col= 0, parse_dates= True)"
   ]
  },
  {
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>3492.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-02-01</th>\n",
       "      <td>3489.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-03-01</th>\n",
       "      <td>3491.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-04-01</th>\n",
       "      <td>3499.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-01</th>\n",
       "      <td>3524.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>4851.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-02-01</th>\n",
       "      <td>4850.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-03-01</th>\n",
       "      <td>4885.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-04-01</th>\n",
       "      <td>4890.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-01</th>\n",
       "      <td>4933.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['1995-01-01', '1995-02-01', '1995-03-01', '1995-04-01',\n",
      "               '1995-05-01', '1995-06-01', '1995-07-01', '1995-08-01',\n",
      "               '1995-09-01', '1995-10-01',\n",
      "               ...\n",
      "               '2015-03-01', '2015-04-01', '2015-05-01', '2015-06-01',\n",
      "               '2015-07-01', '2015-08-01', '2015-09-01', '2015-10-01',\n",
      "               '2015-11-01', '2015-12-01'],\n",
      "              dtype='datetime64[ns]', name='Date', length=252, freq=None)\n",
      "DatetimeIndex(['1995-01-01', '1995-02-01', '1995-03-01', '1995-04-01',\n",
      "               '1995-05-01', '1995-06-01', '1995-07-01', '1995-08-01',\n",
      "               '1995-09-01', '1995-10-01',\n",
      "               ...\n",
      "               '2015-03-01', '2015-04-01', '2015-05-01', '2015-06-01',\n",
      "               '2015-07-01', '2015-08-01', '2015-09-01', '2015-10-01',\n",
      "               '2015-11-01', '2015-12-01'],\n",
      "              dtype='datetime64[ns]', name='Date', length=252, freq=None)\n"
     ]
    }
   ],
   "source": [
    "print(df1.index)\n",
    "print(df2.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.index.freq = 'MS'\n",
    "df2.index.freq = 'MS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['1995-01-01', '1995-02-01', '1995-03-01', '1995-04-01',\n",
      "               '1995-05-01', '1995-06-01', '1995-07-01', '1995-08-01',\n",
      "               '1995-09-01', '1995-10-01',\n",
      "               ...\n",
      "               '2015-03-01', '2015-04-01', '2015-05-01', '2015-06-01',\n",
      "               '2015-07-01', '2015-08-01', '2015-09-01', '2015-10-01',\n",
      "               '2015-11-01', '2015-12-01'],\n",
      "              dtype='datetime64[ns]', name='Date', length=252, freq='MS')\n",
      "DatetimeIndex(['1995-01-01', '1995-02-01', '1995-03-01', '1995-04-01',\n",
      "               '1995-05-01', '1995-06-01', '1995-07-01', '1995-08-01',\n",
      "               '1995-09-01', '1995-10-01',\n",
      "               ...\n",
      "               '2015-03-01', '2015-04-01', '2015-05-01', '2015-06-01',\n",
      "               '2015-07-01', '2015-08-01', '2015-09-01', '2015-10-01',\n",
      "               '2015-11-01', '2015-12-01'],\n",
      "              dtype='datetime64[ns]', name='Date', length=252, freq='MS')\n"
     ]
    }
   ],
   "source": [
    "print(df1.index)\n",
    "print(df2.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.api import VAR\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tools.eval_measures import rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine the dataframe\n",
    "data = df1.join(df2)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(252, 2)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "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>2015-08-01</th>\n",
       "      <td>12096.8</td>\n",
       "      <td>12394.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>12153.8</td>\n",
       "      <td>12392.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>12187.7</td>\n",
       "      <td>12416.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>12277.4</td>\n",
       "      <td>12450.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>12335.9</td>\n",
       "      <td>12469.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Money  Spending\n",
       "Date                         \n",
       "2015-08-01  12096.8   12394.0\n",
       "2015-09-01  12153.8   12392.8\n",
       "2015-10-01  12187.7   12416.1\n",
       "2015-11-01  12277.4   12450.1\n",
       "2015-12-01  12335.9   12469.1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Money       0\n",
       "Spending    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 252 entries, 1995-01-01 to 2015-12-01\n",
      "Freq: MS\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Money     252 non-null    float64\n",
      " 1   Spending  252 non-null    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 15.9 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>count</th>\n",
       "      <td>252.000000</td>\n",
       "      <td>252.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6978.151190</td>\n",
       "      <td>8562.311905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2554.325115</td>\n",
       "      <td>2231.933352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3489.900000</td>\n",
       "      <td>4850.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4732.875000</td>\n",
       "      <td>6682.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6507.400000</td>\n",
       "      <td>8777.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8699.700000</td>\n",
       "      <td>10262.925000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12335.900000</td>\n",
       "      <td>12469.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Money      Spending\n",
       "count    252.000000    252.000000\n",
       "mean    6978.151190   8562.311905\n",
       "std     2554.325115   2231.933352\n",
       "min     3489.900000   4850.800000\n",
       "25%     4732.875000   6682.125000\n",
       "50%     6507.400000   8777.150000\n",
       "75%     8699.700000  10262.925000\n",
       "max    12335.900000  12469.100000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data distribution\n",
    "data.hist(figsize = (15,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(figsize = (15,5),legend = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test for stationarity\n",
    "# augmented dickey-fuller test (adf)\n",
    "\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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: \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(data['Money'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: \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(data['Spending'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_transfomed = data.diff()\n",
    "data_transfomed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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"
     ]
    }
   ],
   "source": [
    "adf_test(data_transfomed['Money'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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": [
    "adf_test(data_transfomed['Spending'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seems to be \"money\" column is not yet stationary\n",
    "data_transfomed = data_transfomed.diff().dropna()\n",
    "data_transfomed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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"
     ]
    }
   ],
   "source": [
    "adf_test(data_transfomed['Money'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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": [
    "adf_test(data_transfomed['Spending'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train and test split\n",
    "nobs = 12 # number of observations\n",
    "train = data_transfomed[ :-nobs]\n",
    "test = data_transfomed[-nobs: ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(238, 2)\n",
      "(12, 2)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "order 0\n",
      "aic value : 14.74710921809045\n",
      "\n",
      "\n",
      "order 1\n",
      "aic value : 14.178610495220896\n",
      "\n",
      "\n",
      "order 2\n",
      "aic value : 13.955189367163705\n",
      "\n",
      "\n",
      "order 3\n",
      "aic value : 13.849518291541038\n",
      "\n",
      "\n",
      "order 4\n",
      "aic value : 13.827950574458283\n",
      "\n",
      "\n",
      "order 5\n",
      "aic value : 13.78730034460964\n",
      "\n",
      "\n",
      "order 6\n",
      "aic value : 13.799076756885809\n",
      "\n",
      "\n",
      "order 7\n",
      "aic value : 13.797638727913972\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# gridsearch for order of VAR - p component\n",
    "# choose low value of AIC\n",
    "model  = VAR(train)\n",
    "\n",
    "for p in range(8):\n",
    "    \n",
    "    results = model.fit(p)\n",
    "    print('order {}'.format(p))\n",
    "    print('aic value : {}'.format(results.aic))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Summary of Regression Results   \n",
       "==================================\n",
       "Model:                         VAR\n",
       "Method:                        OLS\n",
       "Date:           Thu, 23, Jul, 2020\n",
       "Time:                     12:22:13\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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = model.fit(5) # choose order = 5 where AIC is low\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-31.9,  40.3],\n",
       "       [  5.6, -70.7],\n",
       "       [ 25.5,  52.5],\n",
       "       [-21.3, -37.2],\n",
       "       [ 39.5, -17.8]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grab 5 lagged values before test starts\n",
    "# it should be a numpy array\n",
    "lagged_values = train.values[-5:]\n",
    "lagged_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.forecast(y = lagged_values, steps = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = results.forecast(y = lagged_values, steps = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-01', '2015-02-01', '2015-03-01', '2015-04-01',\n",
       "               '2015-05-01', '2015-06-01', '2015-07-01', '2015-08-01',\n",
       "               '2015-09-01', '2015-10-01', '2015-11-01', '2015-12-01'],\n",
       "              dtype='datetime64[ns]', freq='MS')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = pd.date_range('2015-01-01', periods= 12, freq= 'MS') # give test data starting date\n",
    "idx"
   ]
  },
  {
   "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>Money_2d</th>\n",
       "      <th>Spending_2d</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": [
       "             Money_2d  Spending_2d\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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_forecast = pd.DataFrame(data = z, index= idx, columns= ['Money_2d', 'Spending_2d'])\n",
    "df_forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Invert the Transformation\n",
    "- Remember that the forecasted values represent second-order differences. To compare them to the original data we have to roll back each difference. To roll back a first-order difference we take the most recent value on the training side of the original series, and add it to a cumulative sum of forecasted values. When working with second-order differences we first must perform this operation on the most recent first-order difference.\n",
    "\n",
    "- Here we'll use the nobs variable we defined during the train/test/split step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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'] = (data['Money'].iloc[-nobs-1]-data['Money'].iloc[-nobs-2]) + df_forecast['Money_2d'].cumsum()\n",
    "\n",
    "# Now build the forecast values from the first difference set\n",
    "df_forecast['MoneyForecast'] = data['Money'].iloc[-nobs-1] + df_forecast['Money1d'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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'] = (data['Spending'].iloc[-nobs-1]-data['Spending'].iloc[-nobs-2]) + df_forecast['Spending_2d'].cumsum()\n",
    "\n",
    "# Now build the forecast values from the first difference set\n",
    "df_forecast['SpendingForecast'] = data['Spending'].iloc[-nobs-1] + df_forecast['Spending1d'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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_2d</th>\n",
       "      <th>Spending_2d</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": [
       "             Money_2d  Spending_2d    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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('2015-01-01 00:00:00', freq='MS'),\n",
       " Timestamp('2015-02-01 00:00:00', freq='MS'),\n",
       " Timestamp('2015-03-01 00:00:00', freq='MS'),\n",
       " Timestamp('2015-04-01 00:00:00', freq='MS'),\n",
       " Timestamp('2015-05-01 00:00:00', freq='MS'),\n",
       " Timestamp('2015-06-01 00:00:00', freq='MS'),\n",
       " Timestamp('2015-07-01 00:00:00', freq='MS'),\n",
       " Timestamp('2015-08-01 00:00:00', freq='MS'),\n",
       " Timestamp('2015-09-01 00:00:00', freq='MS'),\n",
       " Timestamp('2015-10-01 00:00:00', freq='MS'),\n",
       " Timestamp('2015-11-01 00:00:00', freq='MS'),\n",
       " Timestamp('2015-12-01 00:00:00', freq='MS')]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>11733.2</td>\n",
       "      <td>12046.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>11852.4</td>\n",
       "      <td>12082.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>11868.8</td>\n",
       "      <td>12158.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>11916.1</td>\n",
       "      <td>12193.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>11947.6</td>\n",
       "      <td>12268.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>11993.1</td>\n",
       "      <td>12308.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>12045.3</td>\n",
       "      <td>12355.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>12096.8</td>\n",
       "      <td>12394.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>12153.8</td>\n",
       "      <td>12392.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>12187.7</td>\n",
       "      <td>12416.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>12277.4</td>\n",
       "      <td>12450.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>12335.9</td>\n",
       "      <td>12469.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Money  Spending\n",
       "Date                         \n",
       "2015-01-01  11733.2   12046.0\n",
       "2015-02-01  11852.4   12082.4\n",
       "2015-03-01  11868.8   12158.3\n",
       "2015-04-01  11916.1   12193.8\n",
       "2015-05-01  11947.6   12268.1\n",
       "2015-06-01  11993.1   12308.3\n",
       "2015-07-01  12045.3   12355.4\n",
       "2015-08-01  12096.8   12394.0\n",
       "2015-09-01  12153.8   12392.8\n",
       "2015-10-01  12187.7   12416.1\n",
       "2015-11-01  12277.4   12450.1\n",
       "2015-12-01  12335.9   12469.1"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test data\n",
    "test_range = data[-nobs:]\n",
    "test_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# original test data\n",
    "test_range.plot(figsize = (15,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# forcasted results\n",
    "df_forecast[['MoneyForecast','SpendingForecast']].plot(figsize = (15,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_range['Money'].plot(figsize = (15,5), legend = True)\n",
    "df_forecast['MoneyForecast'].plot(legend = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_range['Spending'].plot(figsize = (15,5), legend = True)\n",
    "df_forecast['SpendingForecast'].plot(legend = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43.71049653558938\n",
      "37.00117516940808\n"
     ]
    }
   ],
   "source": [
    "# error metrics\n",
    "print(rmse(test_range['Money'], df_forecast['MoneyForecast']))\n",
    "print(rmse(test_range['Spending'], df_forecast['SpendingForecast']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}