Section_5_final_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "\n",
    "from __future__ import absolute_import, division, print_function\n",
    "\n",
    "import sys\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.tsa.api as smt\n",
    "\n",
    "import itertools\n",
    "import warnings\n",
    "\n",
    "# Display and Plotting\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%.5f' % x) # pandas\n",
    "np.set_printoptions(precision=5, suppress=True) # numpy\n",
    "\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.max_rows', 100)\n",
    "\n",
    "# seaborn plotting style\n",
    "sns.set(style='ticks', context='poster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_stationarity(timeseries,\n",
    "                      maxlag=None, regression=None, autolag=None,\n",
    "                      window=None, plot=False, verbose=False):\n",
    "    '''\n",
    "    Check unit root stationarity of time series.\n",
    "    \n",
    "    Null hypothesis: the series is non-stationary.\n",
    "    If p >= alpha, the series is non-stationary.\n",
    "    If p < alpha, reject the null hypothesis (has unit root stationarity).\n",
    "    \n",
    "    Original source: http://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/\n",
    "    \n",
    "    Function: http://statsmodels.sourceforge.net/devel/generated/statsmodels.tsa.stattools.adfuller.html\n",
    "    \n",
    "    window argument is only required for plotting rolling functions. Default=4.\n",
    "    '''\n",
    "    \n",
    "    # set defaults (from function page)\n",
    "    if regression is None:\n",
    "        regression = 'c'\n",
    "    \n",
    "    if verbose:\n",
    "        print('Running Augmented Dickey-Fuller test with paramters:')\n",
    "        print('maxlag: {}'.format(maxlag))\n",
    "        print('regression: {}'.format(regression))\n",
    "        print('autolag: {}'.format(autolag))\n",
    "    \n",
    "    if plot:\n",
    "        if window is None:\n",
    "            window = 4\n",
    "        #Determing rolling statistics\n",
    "        rolmean = timeseries.rolling(window=window, center=False).mean()\n",
    "        rolstd = timeseries.rolling(window=window, center=False).std()\n",
    "        \n",
    "        #Plot rolling statistics:\n",
    "        orig = plt.plot(timeseries, color='blue', label='Original')\n",
    "        mean = plt.plot(rolmean, color='red', label='Rolling Mean ({})'.format(window))\n",
    "        std = plt.plot(rolstd, color='black', label='Rolling Std ({})'.format(window))\n",
    "        plt.legend(loc='best')\n",
    "        plt.title('Rolling Mean & Standard Deviation')\n",
    "        plt.show(block=False)\n",
    "    \n",
    "    #Perform Augmented Dickey-Fuller test:\n",
    "    dftest = smt.adfuller(timeseries, maxlag=maxlag, regression=regression, autolag=autolag)\n",
    "    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic',\n",
    "                                             'p-value',\n",
    "                                             '#Lags Used',\n",
    "                                             'Number of Observations Used',\n",
    "                                            ])\n",
    "    for key,value in dftest[4].items():\n",
    "        dfoutput['Critical Value (%s)'%key] = value\n",
    "    if verbose:\n",
    "        print('Results of Augmented Dickey-Fuller Test:')\n",
    "        print(dfoutput)\n",
    "    return dfoutput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tsplot(y, lags=None, title='', figsize=(14, 8)):\n",
    "    '''Examine the patterns of ACF and PACF, along with the time series plot and histogram.\n",
    "    \n",
    "    Original source: https://tomaugspurger.github.io/modern-7-timeseries.html\n",
    "    '''\n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    layout = (2, 2)\n",
    "    ts_ax   = plt.subplot2grid(layout, (0, 0))\n",
    "    hist_ax = plt.subplot2grid(layout, (0, 1))\n",
    "    acf_ax  = plt.subplot2grid(layout, (1, 0))\n",
    "    pacf_ax = plt.subplot2grid(layout, (1, 1))\n",
    "    \n",
    "    y.plot(ax=ts_ax)\n",
    "    ts_ax.set_title(title)\n",
    "    y.plot(ax=hist_ax, kind='hist', bins=25)\n",
    "    hist_ax.set_title('Histogram')\n",
    "    smt.graphics.plot_acf(y, lags=lags, ax=acf_ax)\n",
    "    smt.graphics.plot_pacf(y, lags=lags, ax=pacf_ax)\n",
    "    [ax.set_xlim(0) for ax in [acf_ax, pacf_ax]]\n",
    "    sns.despine()\n",
    "    fig.tight_layout()\n",
    "    return ts_ax, acf_ax, pacf_ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_resid_stats(model_results,\n",
    "                      het_method='breakvar',\n",
    "                      norm_method='jarquebera',\n",
    "                      sercor_method='ljungbox',\n",
    "                      verbose=True,\n",
    "                      ):\n",
    "    '''More information about the statistics under the ARIMA parameters table, tests of standardized residuals:\n",
    "    \n",
    "    Test of heteroskedasticity\n",
    "    http://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAXResults.test_heteroskedasticity.html#statsmodels.tsa.statespace.sarimax.SARIMAXResults.test_heteroskedasticity\n",
    "\n",
    "    Test of normality (Default: Jarque-Bera)\n",
    "    http://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAXResults.test_normality.html#statsmodels.tsa.statespace.sarimax.SARIMAXResults.test_normality\n",
    "\n",
    "    Test of serial correlation (Default: Ljung-Box)\n",
    "    http://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAXResults.test_serial_correlation.html\n",
    "    '''\n",
    "    # Re-run the ARIMA model statistical tests, and more. To be used when selecting viable models.\n",
    "    (het_stat, het_p) = model_results.test_heteroskedasticity(het_method)[0]\n",
    "    norm_stat, norm_p, skew, kurtosis = model_results.test_normality(norm_method)[0]\n",
    "    sercor_stat, sercor_p = model_results.test_serial_correlation(method=sercor_method)[0]\n",
    "    sercor_stat = sercor_stat[-1] # last number for the largest lag\n",
    "    sercor_p = sercor_p[-1] # last number for the largest lag\n",
    "\n",
    "    # Run Durbin-Watson test on the standardized residuals.\n",
    "    # The statistic is approximately equal to 2*(1-r), where r is the sample autocorrelation of the residuals.\n",
    "    # Thus, for r == 0, indicating no serial correlation, the test statistic equals 2.\n",
    "    # This statistic will always be between 0 and 4. The closer to 0 the statistic,\n",
    "    # the more evidence for positive serial correlation. The closer to 4,\n",
    "    # the more evidence for negative serial correlation.\n",
    "    # Essentially, below 1 or above 3 is bad.\n",
    "    dw_stat = sm.stats.stattools.durbin_watson(model_results.filter_results.standardized_forecasts_error[0, model_results.loglikelihood_burn:])\n",
    "\n",
    "    # check whether roots are outside the unit circle (we want them to be);\n",
    "    # will be True when AR is not used (i.e., AR order = 0)\n",
    "    arroots_outside_unit_circle = np.all(np.abs(model_results.arroots) > 1)\n",
    "    # will be True when MA is not used (i.e., MA order = 0)\n",
    "    maroots_outside_unit_circle = np.all(np.abs(model_results.maroots) > 1)\n",
    "    \n",
    "    if verbose:\n",
    "        print('Test heteroskedasticity of residuals ({}): stat={:.3f}, p={:.3f}'.format(het_method, het_stat, het_p));\n",
    "        print('\\nTest normality of residuals ({}): stat={:.3f}, p={:.3f}'.format(norm_method, norm_stat, norm_p));\n",
    "        print('\\nTest serial correlation of residuals ({}): stat={:.3f}, p={:.3f}'.format(sercor_method, sercor_stat, sercor_p));\n",
    "        print('\\nDurbin-Watson test on residuals: d={:.2f}\\n\\t(NB: 2 means no serial correlation, 0=pos, 4=neg)'.format(dw_stat))\n",
    "        print('\\nTest for all AR roots outside unit circle (>1): {}'.format(arroots_outside_unit_circle))\n",
    "        print('\\nTest for all MA roots outside unit circle (>1): {}'.format(maroots_outside_unit_circle))\n",
    "    \n",
    "    stat = {'het_method': het_method,\n",
    "            'het_stat': het_stat,\n",
    "            'het_p': het_p,\n",
    "            'norm_method': norm_method,\n",
    "            'norm_stat': norm_stat,\n",
    "            'norm_p': norm_p,\n",
    "            'skew': skew,\n",
    "            'kurtosis': kurtosis,\n",
    "            'sercor_method': sercor_method,\n",
    "            'sercor_stat': sercor_stat,\n",
    "            'sercor_p': sercor_p,\n",
    "            'dw_stat': dw_stat,\n",
    "            'arroots_outside_unit_circle': arroots_outside_unit_circle,\n",
    "            'maroots_outside_unit_circle': maroots_outside_unit_circle,\n",
    "            }\n",
    "    return stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_gridsearch(ts,\n",
    "                     p_min,\n",
    "                     d_min,\n",
    "                     q_min,\n",
    "                     p_max,\n",
    "                     d_max,\n",
    "                     q_max,\n",
    "                     sP_min,\n",
    "                     sD_min,\n",
    "                     sQ_min,\n",
    "                     sP_max,\n",
    "                     sD_max,\n",
    "                     sQ_max,\n",
    "                     trends,\n",
    "                     s=None,\n",
    "                     enforce_stationarity=True,\n",
    "                     enforce_invertibility=True,\n",
    "                     simple_differencing=False,\n",
    "                     plot_diagnostics=False,\n",
    "                     verbose=False,\n",
    "                     filter_warnings=True,\n",
    "                    ):\n",
    "    '''Run grid search of SARIMAX models and save results.\n",
    "    '''\n",
    "    \n",
    "    cols = ['p', 'd', 'q', 'sP', 'sD', 'sQ', 's', 'trend',\n",
    "            'enforce_stationarity', 'enforce_invertibility', 'simple_differencing',\n",
    "            'aic', 'bic',\n",
    "            'het_p', 'norm_p', 'sercor_p', 'dw_stat',\n",
    "            'arroots_gt_1', 'maroots_gt_1',\n",
    "            'datetime_run']\n",
    "\n",
    "    # Initialize a DataFrame to store the results\n",
    "    df_results = pd.DataFrame(columns=cols)\n",
    "\n",
    "    # # Initialize a DataFrame to store the results\n",
    "    # results_bic = pd.DataFrame(index=['AR{}'.format(i) for i in range(p_min,p_max+1)],\n",
    "    #                            columns=['MA{}'.format(i) for i in range(q_min,q_max+1)])\n",
    "\n",
    "    mod_num=0\n",
    "    for trend,p,d,q,sP,sD,sQ in itertools.product(trends,\n",
    "                                                  range(p_min,p_max+1),\n",
    "                                                  range(d_min,d_max+1),\n",
    "                                                  range(q_min,q_max+1),\n",
    "                                                  range(sP_min,sP_max+1),\n",
    "                                                  range(sD_min,sD_max+1),\n",
    "                                                  range(sQ_min,sQ_max+1),\n",
    "                                                  ):\n",
    "        # initialize to store results for this parameter set\n",
    "        this_model = pd.DataFrame(index=[mod_num], columns=cols)\n",
    "\n",
    "        if p==0 and d==0 and q==0:\n",
    "            continue\n",
    "\n",
    "        try:\n",
    "            model = sm.tsa.SARIMAX(ts,\n",
    "                                   trend=trend,\n",
    "                                   order=(p, d, q),\n",
    "                                   seasonal_order=(sP, sD, sQ, s),\n",
    "                                   enforce_stationarity=enforce_stationarity,\n",
    "                                   enforce_invertibility=enforce_invertibility,\n",
    "                                   simple_differencing=simple_differencing,\n",
    "                                  )\n",
    "            \n",
    "            if filter_warnings is True:\n",
    "                with warnings.catch_warnings():\n",
    "                    warnings.filterwarnings(\"ignore\")\n",
    "                    model_results = model.fit(disp=0)\n",
    "            else:\n",
    "                model_results = model.fit()\n",
    "\n",
    "            if verbose:\n",
    "                print(model_results.summary())\n",
    "\n",
    "            if plot_diagnostics:\n",
    "                model_results.plot_diagnostics();\n",
    "\n",
    "            stat = model_resid_stats(model_results,\n",
    "                                     verbose=verbose)\n",
    "\n",
    "            this_model.loc[mod_num, 'p'] = p\n",
    "            this_model.loc[mod_num, 'd'] = d\n",
    "            this_model.loc[mod_num, 'q'] = q\n",
    "            this_model.loc[mod_num, 'sP'] = sP\n",
    "            this_model.loc[mod_num, 'sD'] = sD\n",
    "            this_model.loc[mod_num, 'sQ'] = sQ\n",
    "            this_model.loc[mod_num, 's'] = s\n",
    "            this_model.loc[mod_num, 'trend'] = trend\n",
    "            this_model.loc[mod_num, 'enforce_stationarity'] = enforce_stationarity\n",
    "            this_model.loc[mod_num, 'enforce_invertibility'] = enforce_invertibility\n",
    "            this_model.loc[mod_num, 'simple_differencing'] = simple_differencing\n",
    "\n",
    "            this_model.loc[mod_num, 'aic'] = model_results.aic\n",
    "            this_model.loc[mod_num, 'bic'] = model_results.bic\n",
    "\n",
    "            # this_model.loc[mod_num, 'het_method'] = stat['het_method']\n",
    "            # this_model.loc[mod_num, 'het_stat'] = stat['het_stat']\n",
    "            this_model.loc[mod_num, 'het_p'] = stat['het_p']\n",
    "            # this_model.loc[mod_num, 'norm_method'] = stat['norm_method']\n",
    "            # this_model.loc[mod_num, 'norm_stat'] = stat['norm_stat']\n",
    "            this_model.loc[mod_num, 'norm_p'] = stat['norm_p']\n",
    "            # this_model.loc[mod_num, 'skew'] = stat['skew']\n",
    "            # this_model.loc[mod_num, 'kurtosis'] = stat['kurtosis']\n",
    "            # this_model.loc[mod_num, 'sercor_method'] = stat['sercor_method']\n",
    "            # this_model.loc[mod_num, 'sercor_stat'] = stat['sercor_stat']\n",
    "            this_model.loc[mod_num, 'sercor_p'] = stat['sercor_p']\n",
    "            this_model.loc[mod_num, 'dw_stat'] = stat['dw_stat']\n",
    "            this_model.loc[mod_num, 'arroots_gt_1'] = stat['arroots_outside_unit_circle']\n",
    "            this_model.loc[mod_num, 'maroots_gt_1'] = stat['maroots_outside_unit_circle']\n",
    "\n",
    "            this_model.loc[mod_num, 'datetime_run'] = pd.to_datetime('today').strftime('%Y-%m-%d %H:%M:%S')\n",
    "\n",
    "            df_results = df_results.append(this_model)\n",
    "            mod_num+=1\n",
    "        except:\n",
    "            continue\n",
    "    return df_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load time series\n",
    "liquor = pd.read_csv('data/liquor.csv', header=0, index_col=0, parse_dates=[0])\n",
    "\n",
    "# Keey only the data from the last 10 years or so\n",
    "liquor = liquor.ix['2007':'2016']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PljpFdFW7dOkS3r17xywL/h+q+ph16NABHTp0kGWTRWKPkFZTU4O6ujoePHiA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\nsWXLFk4noOnTp0tVea8sa2trZoR3Xl4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PHi2paibxz3/+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AZhZ39gwKW9YGel7xiKKseLfrV8/3XwtqQFxZDPhvbnF5TYdqmV6vQ46HUHwqk7WhHtLQhrpM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\nGxgWtpOfmRP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BUAADBeVq/dFms6Ooc9C7n/8TejfXlbrFwxp4yZ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\nK+b4ZrwmNXv/Sb2phzGv9wDUu6r6LfbDH/4wEolERETcf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x107a287f0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 547,
       "width": 965
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "tsplot(liquor['Value'], title='Liquor Sales (in millions of dollars), 2007-2016', lags=40);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Test Statistic                 2.70921\n",
       "p-value                        0.99909\n",
       "#Lags Used                    13.00000\n",
       "Number of Observations Used   99.00000\n",
       "Critical Value (1%)           -3.49820\n",
       "Critical Value (5%)           -2.89121\n",
       "Critical Value (10%)          -2.58260\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test stationarity\n",
    "\n",
    "test_stationarity(liquor['Value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Test Statistic                -2.87507\n",
       "p-value                        0.04832\n",
       "#Lags Used                    13.00000\n",
       "Number of Observations Used   98.00000\n",
       "Critical Value (1%)           -3.49891\n",
       "Critical Value (5%)           -2.89152\n",
       "Critical Value (10%)          -2.58276\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take first difference of the series\n",
    "test_stationarity(liquor['Value'].diff().dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Test Statistic                 1.93490\n",
       "p-value                        0.99858\n",
       "#Lags Used                    13.00000\n",
       "Number of Observations Used   99.00000\n",
       "Critical Value (1%)           -3.49820\n",
       "Critical Value (5%)           -2.89121\n",
       "Critical Value (10%)          -2.58260\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take log of the series\n",
    "liquor['lnliquor'] = np.log(liquor)\n",
    "\n",
    "test_stationarity(liquor['lnliquor'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Test Statistic                -3.18521\n",
       "p-value                        0.02086\n",
       "#Lags Used                    13.00000\n",
       "Number of Observations Used   98.00000\n",
       "Critical Value (1%)           -3.49891\n",
       "Critical Value (5%)           -2.89152\n",
       "Critical Value (10%)          -2.58276\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take first difference of the log series\n",
    "liquor_ln_diff = liquor['lnliquor'].diff()\n",
    "liquor_ln_diff = liquor_ln_diff.dropna()\n",
    "\n",
    "test_stationarity(liquor_ln_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# run model grid search\n",
    "\n",
    "p_min = 0\n",
    "d_min = 0\n",
    "q_min = 0\n",
    "p_max = 2\n",
    "d_max = 1\n",
    "q_max = 2\n",
    "\n",
    "sP_min = 0\n",
    "sD_min = 0\n",
    "sQ_min = 0\n",
    "sP_max = 1\n",
    "sD_max = 1\n",
    "sQ_max = 1\n",
    "\n",
    "s=12\n",
    "\n",
    "# trends=['n', 'c']\n",
    "trends=['n']\n",
    "\n",
    "enforce_stationarity=True\n",
    "enforce_invertibility=True\n",
    "simple_differencing=False\n",
    "\n",
    "plot_diagnostics=False\n",
    "\n",
    "verbose=False\n",
    "\n",
    "df_results = model_gridsearch(liquor['Value'],\n",
    "                              p_min,\n",
    "                              d_min,\n",
    "                              q_min,\n",
    "                              p_max,\n",
    "                              d_max,\n",
    "                              q_max,\n",
    "                              sP_min,\n",
    "                              sD_min,\n",
    "                              sQ_min,\n",
    "                              sP_max,\n",
    "                              sD_max,\n",
    "                              sQ_max,\n",
    "                              trends,\n",
    "                              s=s,\n",
    "                              enforce_stationarity=enforce_stationarity,\n",
    "                              enforce_invertibility=enforce_invertibility,\n",
    "                              simple_differencing=simple_differencing,\n",
    "                              plot_diagnostics=plot_diagnostics,\n",
    "                              verbose=verbose,\n",
    "                              )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>p</th>\n",
       "      <th>d</th>\n",
       "      <th>q</th>\n",
       "      <th>sP</th>\n",
       "      <th>sD</th>\n",
       "      <th>sQ</th>\n",
       "      <th>s</th>\n",
       "      <th>trend</th>\n",
       "      <th>enforce_stationarity</th>\n",
       "      <th>enforce_invertibility</th>\n",
       "      <th>simple_differencing</th>\n",
       "      <th>aic</th>\n",
       "      <th>bic</th>\n",
       "      <th>het_p</th>\n",
       "      <th>norm_p</th>\n",
       "      <th>sercor_p</th>\n",
       "      <th>dw_stat</th>\n",
       "      <th>arroots_gt_1</th>\n",
       "      <th>maroots_gt_1</th>\n",
       "      <th>datetime_run</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>n</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1197.41682</td>\n",
       "      <td>1202.87160</td>\n",
       "      <td>0.81931</td>\n",
       "      <td>0.62952</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>2.26474</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-11-07 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>n</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1195.58900</td>\n",
       "      <td>1203.77116</td>\n",
       "      <td>0.62961</td>\n",
       "      <td>0.70785</td>\n",
       "      <td>0.00039</td>\n",
       "      <td>1.97381</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-11-07 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>n</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1196.32530</td>\n",
       "      <td>1204.50746</td>\n",
       "      <td>0.70934</td>\n",
       "      <td>0.38694</td>\n",
       "      <td>0.03983</td>\n",
       "      <td>1.96089</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-11-07 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>n</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1191.51830</td>\n",
       "      <td>1205.15524</td>\n",
       "      <td>0.94794</td>\n",
       "      <td>0.64313</td>\n",
       "      <td>0.01569</td>\n",
       "      <td>1.98726</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-11-07 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>n</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1194.33589</td>\n",
       "      <td>1205.24544</td>\n",
       "      <td>0.90090</td>\n",
       "      <td>0.68711</td>\n",
       "      <td>0.00018</td>\n",
       "      <td>2.32747</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-11-07 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>n</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1194.59151</td>\n",
       "      <td>1205.50106</td>\n",
       "      <td>0.85899</td>\n",
       "      <td>0.65411</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>1.99280</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-11-07 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>n</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1192.24488</td>\n",
       "      <td>1205.88182</td>\n",
       "      <td>0.97214</td>\n",
       "      <td>0.73123</td>\n",
       "      <td>0.06795</td>\n",
       "      <td>1.89388</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2017-11-07 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>n</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1197.92410</td>\n",
       "      <td>1206.10626</td>\n",
       "      <td>0.99177</td>\n",
       "      <td>0.46687</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2.32319</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-11-07 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>n</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1195.36618</td>\n",
       "      <td>1206.27573</td>\n",
       "      <td>0.78559</td>\n",
       "      <td>0.65805</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>1.98916</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-11-07 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>n</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1195.48942</td>\n",
       "      <td>1206.39897</td>\n",
       "      <td>0.51740</td>\n",
       "      <td>0.39251</td>\n",
       "      <td>0.09226</td>\n",
       "      <td>1.97863</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-11-07 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    p  d  q sP sD sQ   s trend enforce_stationarity enforce_invertibility  \\\n",
       "16  0  1  1  0  1  0  12     n                 True                  True   \n",
       "33  1  1  1  0  1  0  12     n                 True                  True   \n",
       "54  2  1  0  0  1  0  12     n                 True                  True   \n",
       "36  1  1  1  1  1  1  12     n                 True                  True   \n",
       "19  0  1  1  1  1  1  12     n                 True                  True   \n",
       "34  1  1  1  0  1  1  12     n                 True                  True   \n",
       "64  2  1  2  0  1  0  12     n                 True                  True   \n",
       "17  0  1  1  0  1  1  12     n                 True                  True   \n",
       "35  1  1  1  1  1  0  12     n                 True                  True   \n",
       "55  2  1  0  0  1  1  12     n                 True                  True   \n",
       "\n",
       "   simple_differencing        aic        bic   het_p  norm_p sercor_p dw_stat  \\\n",
       "16               False 1197.41682 1202.87160 0.81931 0.62952  0.00001 2.26474   \n",
       "33               False 1195.58900 1203.77116 0.62961 0.70785  0.00039 1.97381   \n",
       "54               False 1196.32530 1204.50746 0.70934 0.38694  0.03983 1.96089   \n",
       "36               False 1191.51830 1205.15524 0.94794 0.64313  0.01569 1.98726   \n",
       "19               False 1194.33589 1205.24544 0.90090 0.68711  0.00018 2.32747   \n",
       "34               False 1194.59151 1205.50106 0.85899 0.65411  0.00001 1.99280   \n",
       "64               False 1192.24488 1205.88182 0.97214 0.73123  0.06795 1.89388   \n",
       "17               False 1197.92410 1206.10626 0.99177 0.46687  0.00000 2.32319   \n",
       "35               False 1195.36618 1206.27573 0.78559 0.65805  0.00001 1.98916   \n",
       "55               False 1195.48942 1206.39897 0.51740 0.39251  0.09226 1.97863   \n",
       "\n",
       "   arroots_gt_1 maroots_gt_1         datetime_run  \n",
       "16         True         True  2017-11-07 00:00:00  \n",
       "33         True         True  2017-11-07 00:00:00  \n",
       "54         True         True  2017-11-07 00:00:00  \n",
       "36         True         True  2017-11-07 00:00:00  \n",
       "19         True         True  2017-11-07 00:00:00  \n",
       "34         True         True  2017-11-07 00:00:00  \n",
       "64         True        False  2017-11-07 00:00:00  \n",
       "17         True         True  2017-11-07 00:00:00  \n",
       "35         True         True  2017-11-07 00:00:00  \n",
       "55         True         True  2017-11-07 00:00:00  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# choose a model\n",
    "\n",
    "df_results.sort_values(by='bic').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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