time_series.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from datetime import datetime\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017\n"
     ]
    }
   ],
   "source": [
    "with open('seasons.pickle') as f:\n",
    "    seasons = pickle.load(f)\n",
    "for year, season in seasons.iteritems():\n",
    "    print year,\n",
    "    season.load_scores()\n",
    "with open('season2018.pickle') as f:\n",
    "    season18 = pickle.load(f)\n",
    "season18.load_scores()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "skaters = {}\n",
    "for discipline in ('men', 'ladies', 'pairs', 'dance'):\n",
    "    skaters[discipline] = pd.read_csv('pd_data/results_nowd_nofra15_' + discipline + '.csv')\n",
    "    skaters[discipline].Date = skaters[discipline].Date.astype('datetime64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ts = skaters['men'][['Date', 'Points', 'Name']]\n",
    "ts = ts.set_index('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "drop_skater = []\n",
    "for skater in ts.Name.unique():\n",
    "    skater_ts = ts[ts.Name == skater]\n",
    "    if len(skater_ts) < 11 and all(skater_ts.index) < '2014':\n",
    "        drop_skater.append(skater)\n",
    "for skater in drop_skater:\n",
    "    ts = ts[ts.Name != skater]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from statsmodels.tsa.stattools import adfuller\n",
    "# def test_stationarity(timeseries):\n",
    "    \n",
    "#     #Determing rolling statistics\n",
    "#     rolling_mean = timeseries.rolling(window=5, center=False)\n",
    "#     rolmean = rolling_mean.mean()\n",
    "#     rolstd = rolling_mean.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')\n",
    "#     std = plt.plot(rolstd, color='black', label = 'Rolling Std')\n",
    "#     plt.legend(loc='best')\n",
    "#     plt.title('Rolling Mean & Standard Deviation')\n",
    "#     plt.show(block=False)\n",
    "    \n",
    "#     #Perform Dickey-Fuller test:\n",
    "#     print 'Results of Dickey-Fuller Test:'\n",
    "#     dftest = adfuller(timeseries, autolag='AIC')\n",
    "#     dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "#     for key,value in dftest[4].items():\n",
    "#         dfoutput['Critical Value (%s)'%key] = value\n",
    "#     print dfoutput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# num_obs = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# # for skater in ts.Name.unique():\n",
    "#     print\n",
    "#     print skater\n",
    "#     skater_ts = ts[ts.Name == skater].Points\n",
    "#     ewa = pd.ewma(skater_ts, halflife=5)\n",
    "#     moving_avg = pd.rolling_mean(skater_ts, 5)\n",
    "#     diff = skater_ts - ewa\n",
    "#     diff.dropna(inplace=True)\n",
    "#     test_stationarity(diff)\n",
    "#     plt.show()\n",
    "#     num_obs[skater] = len(skater_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "skater = 'Shoma UNO'\n",
    "skater_ts = ts[ts.Name == skater].Points\n",
    "# ewa = skater_ts.ewm(halflife=5, ignore_na=False, min_periods=0, adjust=True).mean()\n",
    "# # moving_avg = pd.rolling_mean(skater_ts, 5)\n",
    "# diff = skater_ts - ewa\n",
    "# diff.dropna(inplace=True)\n",
    "# test_stationarity(diff)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "# define the p, d and q parameters to take any value between 0 and 2\n",
    "p = d = q = xrange(0, 3)\n",
    " \n",
    "# generate all different combinations of p, d and q triplets\n",
    "pdq = list(itertools.product(p, d, q))\n",
    "pdq = [x for x in pdq if x[0] != 0]\n",
    "\n",
    "# generate all different combinations of seasonal p, q and q triplets\n",
    "seasonal_pdq = [(x[0], x[1], x[2], 12) for x in pdq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python2.7/site-packages/statsmodels/tsa/statespace/sarimax.py:928: RuntimeWarning: Mean of empty slice.\n",
      "  params_variance = (residuals[k_params_ma:]**2).mean()\n",
      "/anaconda/lib/python2.7/site-packages/numpy/core/_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Unexpected error: <type 'exceptions.ValueError'>\n",
      "Best SARIMAXNonexNone12 model - AIC:inf\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "best_aic = np.inf\n",
    "best_pdq = None\n",
    "best_seasonal_pdq = None\n",
    "tmp_model = None\n",
    "best_mdl = None\n",
    " \n",
    "for param in pdq:\n",
    "    for param_seasonal in seasonal_pdq:\n",
    "        try:\n",
    "            tmp_mdl = sm.tsa.statespace.SARIMAX(skater_ts,\n",
    "                                                order = param,\n",
    "                                                seasonal_order = param_seasonal,\n",
    "                                                enforce_stationarity=True,\n",
    "                                                enforce_invertibility=True)\n",
    "            res = tmp_mdl.fit()\n",
    "            if res.aic < best_aic:\n",
    "                best_aic = res.aic\n",
    "                best_pdq = param\n",
    "                best_seasonal_pdq = param_seasonal\n",
    "                best_mdl = tmp_mdl\n",
    "        except:\n",
    "            print \"Unexpected error:\", sys.exc_info()[0]\n",
    "            continue\n",
    "print \"Best SARIMAX{}x{}12 model - AIC:{}\".format(best_pdq, best_seasonal_pdq, best_aic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mdl = sm.tsa.statespace.SARIMAX(skater_ts.values,\n",
    "                                order=(2, 1, 1),\n",
    "                                seasonal_order=(1, 2, 0, 12),\n",
    "                                enforce_stationarity=True,\n",
    "                                enforce_invertibility=True)\n",
    "res = mdl.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Statespace Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                  <td>y</td>               <th>  No. Observations:  </th>   <td>40</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(2, 1, 1)x(1, 2, 0, 12)</td> <th>  Log Likelihood     </th> <td>-71.563</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Fri, 09 Mar 2018</td>        <th>  AIC                </th> <td>153.127</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>09:28:13</td>            <th>  BIC                </th> <td>161.571</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                         <td>0</td>               <th>  HQIC               </th> <td>156.180</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td> - 40</td>             <th>                     </th>    <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>               <td>opg</td>              <th>                     </th>    <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>    <td>   -0.2726</td> <td>    0.316</td> <td>   -0.862</td> <td> 0.389</td> <td>   -0.893</td> <td>    0.347</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>    <td>   -0.5142</td> <td>    0.254</td> <td>   -2.022</td> <td> 0.043</td> <td>   -1.013</td> <td>   -0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>    <td>   -0.9908</td> <td>   10.312</td> <td>   -0.096</td> <td> 0.923</td> <td>  -21.201</td> <td>   19.220</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th> <td>   -0.2133</td> <td>    0.668</td> <td>   -0.319</td> <td> 0.750</td> <td>   -1.523</td> <td>    1.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td>  587.1905</td> <td> 5910.274</td> <td>    0.099</td> <td> 0.921</td> <td> -1.1e+04</td> <td> 1.22e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>7.46</td> <th>  Jarque-Bera (JB):  </th> <td>0.90</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.92</td> <th>  Prob(JB):          </th> <td>0.64</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.23</td> <th>  Skew:              </th> <td>-0.41</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.14</td> <th>  Kurtosis:          </th> <td>2.12</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 Statespace Model Results                                 \n",
       "==========================================================================================\n",
       "Dep. Variable:                                  y   No. Observations:                   40\n",
       "Model:             SARIMAX(2, 1, 1)x(1, 2, 0, 12)   Log Likelihood                 -71.563\n",
       "Date:                            Fri, 09 Mar 2018   AIC                            153.127\n",
       "Time:                                    09:28:13   BIC                            161.571\n",
       "Sample:                                         0   HQIC                           156.180\n",
       "                                             - 40                                         \n",
       "Covariance Type:                              opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1         -0.2726      0.316     -0.862      0.389      -0.893       0.347\n",
       "ar.L2         -0.5142      0.254     -2.022      0.043      -1.013      -0.016\n",
       "ma.L1         -0.9908     10.312     -0.096      0.923     -21.201      19.220\n",
       "ar.S.L12      -0.2133      0.668     -0.319      0.750      -1.523       1.096\n",
       "sigma2       587.1905   5910.274      0.099      0.921    -1.1e+04    1.22e+04\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                        7.46   Jarque-Bera (JB):                 0.90\n",
       "Prob(Q):                              0.92   Prob(JB):                         0.64\n",
       "Heteroskedasticity (H):               0.23   Skew:                            -0.41\n",
       "Prob(H) (two-sided):                  0.14   Kurtosis:                         2.12\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.801849568201831"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chi2\n",
    "chi2.isf(q=0.05, df=40-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RkpOTu93LrQmvUkoppZRSSoWZ/pzsNgrGZ6AJr1JKKaWUUkqpPkkTXqVUjzDGsDGvzOkw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UAAAg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\nK0v/H2+NIQ8lTQriAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1155cf5d0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LTXi7YWt+BXXeADNHhWfCC3DyuFR+9f1JvLu5kEc+2O50OKqf2FZYScDAxBYK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UAAAg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\nK0v/H2+NIQ8lTQriAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1155cf5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res.plot_diagnostics(figsize=(16, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 309.04084969,  262.61349004,  284.48467221,  278.77804546,\n",
       "        252.26040447])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = res.get_forecast(steps=5)\n",
    "pred.predicted_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_results = pd.read_csv('pd_data/results18_men.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "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>Unnamed: 0</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Event</th>\n",
       "      <th>Date</th>\n",
       "      <th>Name</th>\n",
       "      <th>Nation</th>\n",
       "      <th>Points</th>\n",
       "      <th>Short Rank</th>\n",
       "      <th>Short Score</th>\n",
       "      <th>Free Rank</th>\n",
       "      <th>Free Score</th>\n",
       "      <th>Short Start</th>\n",
       "      <th>Free Start</th>\n",
       "      <th>Num Short Scorecards</th>\n",
       "      <th>Num Free Scorecards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>gpcan2017</td>\n",
       "      <td>2017-10-29</td>\n",
       "      <td>Patrick CHAN</td>\n",
       "      <td>CAN</td>\n",
       "      <td>245.7</td>\n",
       "      <td>2</td>\n",
       "      <td>94.43</td>\n",
       "      <td>7</td>\n",
       "      <td>151.27</td>\n",
       "      <td>11</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  Rank      Event        Date          Name Nation  Points  \\\n",
       "15           3     4  gpcan2017  2017-10-29  Patrick CHAN    CAN   245.7   \n",
       "\n",
       "    Short Rank  Short Score Free Rank Free Score  Short Start  Free Start  \\\n",
       "15           2        94.43         7     151.27           11        11.0   \n",
       "\n",
       "    Num Short Scorecards  Num Free Scorecards  \n",
       "15                    12                   12  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_results[new_results.Name == 'Patrick CHAN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import acf, pacf\n",
    "lag_acf = acf(diff, nlags=20)\n",
    "lag_pacf = pacf(diff, nlags=20, method='ols')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PN3cyEZkvItkikl1YWOhA9s5WPxbAegIZYyJZsBqB/wjcr6rNzsCmqk+q6kRVnZiSkhKQ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\n4g0FDErpyogALf3YFnPHpLL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x120af08d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x122986710>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WxXVvLCM7v4zfjO3LXdNG+PXIsp17ajn83s+5a9pwLjoq1atldeQ+AE9cARQC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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121524bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lag_acf)\n",
    "plt.axhline(y=0,linestyle='--',color='gray')\n",
    "plt.axhline(y=-1.96/np.sqrt(len(diff)),linestyle='--',color='gray')\n",
    "plt.axhline(y=1.96/np.sqrt(len(diff)),linestyle='--',color='gray')\n",
    "plt.title('Autocorrelation Function')\n",
    "plt.show()\n",
    "plt.plot(lag_pacf)\n",
    "plt.axhline(y=0,linestyle='--',color='gray')\n",
    "plt.axhline(y=-1.96/np.sqrt(len(diff)),linestyle='--',color='gray')\n",
    "plt.axhline(y=1.96/np.sqrt(len(diff)),linestyle='--',color='gray')\n",
    "plt.title('Partial Autocorrelation Function')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x123a8a610>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6SaVn3n9/cotGRfjKogtS3xJg4ad7mTttKN7pU+DrX1dXD/sS72I5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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122c7e210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = ARIMA(skater_ts, order=(2, 1, 2))  \n",
    "results_ARIMA = model.fit(disp=-1)  \n",
    "plt.plot(diff)\n",
    "plt.plot(results_ARIMA.fittedvalues, color='red')\n",
    "plt.title('RSS: %.4f'% sum((results_ARIMA.fittedvalues-diff)**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date\n",
      "2006-12-03     2.311916\n",
      "2007-10-28     5.272989\n",
      "2007-11-18   -14.678835\n",
      "2007-12-16   -21.894426\n",
      "2008-03-23    -5.150524\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "predictions_ARIMA_diff = pd.Series(results_ARIMA.fittedvalues, copy=True)\n",
    "print predictions_ARIMA_diff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date\n",
      "2006-12-03     2.311916\n",
      "2007-10-28     7.584905\n",
      "2007-11-18    -7.093930\n",
      "2007-12-16   -28.988356\n",
      "2008-03-23   -34.138880\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "predictions_ARIMA_diff_cumsum = predictions_ARIMA_diff.cumsum()\n",
    "print predictions_ARIMA_diff_cumsum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}