{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nils/projects/python/tsfresh/venv/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from tsfresh import extract_features\n",
    "from tsfresh.utilities.dataframe_functions import make_forecasting_frame\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from tsfresh.utilities.dataframe_functions import impute\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Construct the signal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just for showing how the forecasting works, we invent our own signal here, that we want to forecast later.\n",
    "It is a mixture of random noise and some sinus graph with a positive and negative slope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-01 00:00:00    0.270096\n",
       "2011-01-01 01:00:00    1.073392\n",
       "2011-01-01 02:00:00    0.274406\n",
       "2011-01-01 03:00:00    1.446233\n",
       "2011-01-01 04:00:00   -0.035727\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_up = np.arange(100)*0.1 + np.sin(30*np.pi*np.linspace(0, 1, 100)) + np.random.normal(scale=0.7,size=100)\n",
    "x_down = np.arange(100, 0, -1)*0.15 + np.sin(30*np.pi*np.linspace(0, 1, 100)) + np.random.normal(scale=0.7,size=100)\n",
    "x = np.concatenate([x_up, x_down])\n",
    "x = pd.Series(data=x, index=pd.date_range('1/1/2011', periods=len(x), freq='H'))\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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+UIiIri8M6IiIqG5Gi6YKrmbJpRrQtdrnT1rcv96FY8OzVb0gjuVLLrmHrrQt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\nAQi3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3b5d7e510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "plt.plot(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, we have a time series and want to construct a time series model that is able to predict the next data points.\n",
    "\n",
    "To do that, we have to construct a feature matrix by calculating the features for sub time series (see the forecasting section in the tsfresh documentation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(x)\n",
    "df.reset_index(inplace=True)\n",
    "df.columns = [\"time\", \"value\"]\n",
    "df[\"kind\"] = \"a\"\n",
    "df[\"id\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tsfresh.utilities.dataframe_functions import roll_time_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_shift, y = make_forecasting_frame(x, kind=\"price\", max_timeshift=10, rolling_direction=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`df_shift` is ready to be passed into the feature extraction process in tsfresh "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|██████████| 199/199 [00:07<00:00, 27.02it/s]\n"
     ]
    }
   ],
   "source": [
    "X = extract_features(df_shift, column_id=\"id\", column_sort=\"time\", column_value=\"value\", impute_function=impute, \n",
    "                     show_warnings=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>variable</th>\n",
       "      <th>feature__abs_energy</th>\n",
       "      <th>feature__absolute_sum_of_changes</th>\n",
       "      <th>feature__agg_autocorrelation__f_agg_\"mean\"</th>\n",
       "      <th>feature__agg_autocorrelation__f_agg_\"median\"</th>\n",
       "      <th>feature__agg_autocorrelation__f_agg_\"var\"</th>\n",
       "      <th>feature__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"intercept\"</th>\n",
       "      <th>feature__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"rvalue\"</th>\n",
       "      <th>feature__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"slope\"</th>\n",
       "      <th>feature__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"stderr\"</th>\n",
       "      <th>feature__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"intercept\"</th>\n",
       "      <th>...</th>\n",
       "      <th>feature__time_reversal_asymmetry_statistic__lag_1</th>\n",
       "      <th>feature__time_reversal_asymmetry_statistic__lag_2</th>\n",
       "      <th>feature__time_reversal_asymmetry_statistic__lag_3</th>\n",
       "      <th>feature__value_count__value_-inf</th>\n",
       "      <th>feature__value_count__value_0</th>\n",
       "      <th>feature__value_count__value_1</th>\n",
       "      <th>feature__value_count__value_inf</th>\n",
       "      <th>feature__value_count__value_nan</th>\n",
       "      <th>feature__variance</th>\n",
       "      <th>feature__variance_larger_than_standard_deviation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>0.072952</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>1.225122</td>\n",
       "      <td>0.803296</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.161321</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>1.300420</td>\n",
       "      <td>1.602281</td>\n",
       "      <td>-0.250011</td>\n",
       "      <td>-0.250011</td>\n",
       "      <td>0.562451</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002519</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>3.392011</td>\n",
       "      <td>2.774108</td>\n",
       "      <td>-0.412896</td>\n",
       "      <td>-0.814122</td>\n",
       "      <td>0.856959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.130151</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.261198</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 05:00:00</th>\n",
       "      <td>3.393288</td>\n",
       "      <td>4.256068</td>\n",
       "      <td>-0.095596</td>\n",
       "      <td>-0.107639</td>\n",
       "      <td>0.679607</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051083</td>\n",
       "      <td>-0.019668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.311809</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 550 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "variable             feature__abs_energy  feature__absolute_sum_of_changes  \\\n",
       "id                                                                           \n",
       "2011-01-01 01:00:00             0.072952                          0.000000   \n",
       "2011-01-01 02:00:00             1.225122                          0.803296   \n",
       "2011-01-01 03:00:00             1.300420                          1.602281   \n",
       "2011-01-01 04:00:00             3.392011                          2.774108   \n",
       "2011-01-01 05:00:00             3.393288                          4.256068   \n",
       "\n",
       "variable             feature__agg_autocorrelation__f_agg_\"mean\"  \\\n",
       "id                                                                \n",
       "2011-01-01 01:00:00                                    0.000000   \n",
       "2011-01-01 02:00:00                                   -1.000000   \n",
       "2011-01-01 03:00:00                                   -0.250011   \n",
       "2011-01-01 04:00:00                                   -0.412896   \n",
       "2011-01-01 05:00:00                                   -0.095596   \n",
       "\n",
       "variable             feature__agg_autocorrelation__f_agg_\"median\"  \\\n",
       "id                                                                  \n",
       "2011-01-01 01:00:00                                      0.000000   \n",
       "2011-01-01 02:00:00                                     -1.000000   \n",
       "2011-01-01 03:00:00                                     -0.250011   \n",
       "2011-01-01 04:00:00                                     -0.814122   \n",
       "2011-01-01 05:00:00                                     -0.107639   \n",
       "\n",
       "variable             feature__agg_autocorrelation__f_agg_\"var\"  \\\n",
       "id                                                               \n",
       "2011-01-01 01:00:00                                   0.000000   \n",
       "2011-01-01 02:00:00                                   0.000000   \n",
       "2011-01-01 03:00:00                                   0.562451   \n",
       "2011-01-01 04:00:00                                   0.856959   \n",
       "2011-01-01 05:00:00                                   0.679607   \n",
       "\n",
       "variable             feature__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"intercept\"  \\\n",
       "id                                                                                            \n",
       "2011-01-01 01:00:00                                                0.0                        \n",
       "2011-01-01 02:00:00                                                0.0                        \n",
       "2011-01-01 03:00:00                                                0.0                        \n",
       "2011-01-01 04:00:00                                                0.0                        \n",
       "2011-01-01 05:00:00                                                0.0                        \n",
       "\n",
       "variable             feature__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"rvalue\"  \\\n",
       "id                                                                                         \n",
       "2011-01-01 01:00:00                                                0.0                     \n",
       "2011-01-01 02:00:00                                                0.0                     \n",
       "2011-01-01 03:00:00                                                0.0                     \n",
       "2011-01-01 04:00:00                                                0.0                     \n",
       "2011-01-01 05:00:00                                                0.0                     \n",
       "\n",
       "variable             feature__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"slope\"  \\\n",
       "id                                                                                        \n",
       "2011-01-01 01:00:00                                                0.0                    \n",
       "2011-01-01 02:00:00                                                0.0                    \n",
       "2011-01-01 03:00:00                                                0.0                    \n",
       "2011-01-01 04:00:00                                                0.0                    \n",
       "2011-01-01 05:00:00                                                0.0                    \n",
       "\n",
       "variable             feature__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"stderr\"  \\\n",
       "id                                                                                         \n",
       "2011-01-01 01:00:00                                                0.0                     \n",
       "2011-01-01 02:00:00                                                0.0                     \n",
       "2011-01-01 03:00:00                                                0.0                     \n",
       "2011-01-01 04:00:00                                                0.0                     \n",
       "2011-01-01 05:00:00                                                0.0                     \n",
       "\n",
       "variable             feature__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"intercept\"  \\\n",
       "id                                                                                            \n",
       "2011-01-01 01:00:00                                                0.0                        \n",
       "2011-01-01 02:00:00                                                0.0                        \n",
       "2011-01-01 03:00:00                                                0.0                        \n",
       "2011-01-01 04:00:00                                                0.0                        \n",
       "2011-01-01 05:00:00                                                0.0                        \n",
       "\n",
       "variable                                   ...                         \\\n",
       "id                                         ...                          \n",
       "2011-01-01 01:00:00                        ...                          \n",
       "2011-01-01 02:00:00                        ...                          \n",
       "2011-01-01 03:00:00                        ...                          \n",
       "2011-01-01 04:00:00                        ...                          \n",
       "2011-01-01 05:00:00                        ...                          \n",
       "\n",
       "variable             feature__time_reversal_asymmetry_statistic__lag_1  \\\n",
       "id                                                                       \n",
       "2011-01-01 01:00:00                                           0.000000   \n",
       "2011-01-01 02:00:00                                           0.000000   \n",
       "2011-01-01 03:00:00                                           0.002519   \n",
       "2011-01-01 04:00:00                                           0.130151   \n",
       "2011-01-01 05:00:00                                           0.051083   \n",
       "\n",
       "variable             feature__time_reversal_asymmetry_statistic__lag_2  \\\n",
       "id                                                                       \n",
       "2011-01-01 01:00:00                                           0.000000   \n",
       "2011-01-01 02:00:00                                           0.000000   \n",
       "2011-01-01 03:00:00                                           0.000000   \n",
       "2011-01-01 04:00:00                                           0.000000   \n",
       "2011-01-01 05:00:00                                          -0.019668   \n",
       "\n",
       "variable             feature__time_reversal_asymmetry_statistic__lag_3  \\\n",
       "id                                                                       \n",
       "2011-01-01 01:00:00                                                0.0   \n",
       "2011-01-01 02:00:00                                                0.0   \n",
       "2011-01-01 03:00:00                                                0.0   \n",
       "2011-01-01 04:00:00                                                0.0   \n",
       "2011-01-01 05:00:00                                                0.0   \n",
       "\n",
       "variable             feature__value_count__value_-inf  \\\n",
       "id                                                      \n",
       "2011-01-01 01:00:00                               0.0   \n",
       "2011-01-01 02:00:00                               0.0   \n",
       "2011-01-01 03:00:00                               0.0   \n",
       "2011-01-01 04:00:00                               0.0   \n",
       "2011-01-01 05:00:00                               0.0   \n",
       "\n",
       "variable             feature__value_count__value_0  \\\n",
       "id                                                   \n",
       "2011-01-01 01:00:00                            0.0   \n",
       "2011-01-01 02:00:00                            0.0   \n",
       "2011-01-01 03:00:00                            0.0   \n",
       "2011-01-01 04:00:00                            0.0   \n",
       "2011-01-01 05:00:00                            0.0   \n",
       "\n",
       "variable             feature__value_count__value_1  \\\n",
       "id                                                   \n",
       "2011-01-01 01:00:00                            0.0   \n",
       "2011-01-01 02:00:00                            0.0   \n",
       "2011-01-01 03:00:00                            0.0   \n",
       "2011-01-01 04:00:00                            0.0   \n",
       "2011-01-01 05:00:00                            0.0   \n",
       "\n",
       "variable             feature__value_count__value_inf  \\\n",
       "id                                                     \n",
       "2011-01-01 01:00:00                              0.0   \n",
       "2011-01-01 02:00:00                              0.0   \n",
       "2011-01-01 03:00:00                              0.0   \n",
       "2011-01-01 04:00:00                              0.0   \n",
       "2011-01-01 05:00:00                              0.0   \n",
       "\n",
       "variable             feature__value_count__value_nan  feature__variance  \\\n",
       "id                                                                        \n",
       "2011-01-01 01:00:00                              0.0           0.000000   \n",
       "2011-01-01 02:00:00                              0.0           0.161321   \n",
       "2011-01-01 03:00:00                              0.0           0.142631   \n",
       "2011-01-01 04:00:00                              0.0           0.261198   \n",
       "2011-01-01 05:00:00                              0.0           0.311809   \n",
       "\n",
       "variable             feature__variance_larger_than_standard_deviation  \n",
       "id                                                                     \n",
       "2011-01-01 01:00:00                                               0.0  \n",
       "2011-01-01 02:00:00                                               0.0  \n",
       "2011-01-01 03:00:00                                               0.0  \n",
       "2011-01-01 04:00:00                                               0.0  \n",
       "2011-01-01 05:00:00                                               0.0  \n",
       "\n",
       "[5 rows x 550 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, the first row for `id=2011-01-01 01:00:00` contains features that were just calculate on `2011-01-01 00:00:00`. \n",
    "The third row `2011-01-01 03:00:00` contains features that were calculated on `2011-01-01 00:00:00`, `2011-01-01 01:00:00` and `2011-01-01 02:00:00`.\n",
    "\n",
    "However, because we set `max_timeshift` to 10, the features will only be based on a maximum number of 10 historic data points.\n",
    "\n",
    "We are now using the features, to train a normal AdaBoostRegressor to predict the next time step. So for every data point, we fit the model on all older data points, then predict the next data point. Then we fit it on all data points again plus that predicted data point and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "ada = AdaBoostRegressor()\n",
    "\n",
    "y_pred = [0] * len(y)\n",
    "y_pred[0] = y.iloc[0]\n",
    "\n",
    "for i in range(1, len(y)):\n",
    "    ada.fit(X.iloc[:i], y[:i])\n",
    "    y_pred[i] = ada.predict(X.iloc[i, :])\n",
    "    \n",
    "y_pred = pd.Series(data=y_pred, index=y.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = pd.Series(data=y_pred, index=y.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+RXxy1PcLzqILNbogoHATpBbAnl9T1thFtM0IZgrXUILLjGaPb+6Qt71SUo8Z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\nZRsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3b591d590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "plt.plot(y, label=\"true\")\n",
    "plt.plot(y_pred, label=\"predicted\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}