{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 501 時系列データ分析\n",
    "## 読み込み"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T04:23:47.924575Z",
     "start_time": "2018-05-11T04:23:46.703914Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import matplotlib.pyplot as plt  \n",
    "import matplotlib.dates as mdates\n",
    "%matplotlib inline\n",
    "\n",
    "from matplotlib import pyplot\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from statsmodels.tsa.arima_model import ARIMA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 時系列データの整形\n",
    "### 時系列データの入手\n",
    "\n",
    "Don't spam this downloading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T04:23:48.370695Z",
     "start_time": "2018-05-11T04:23:47.926474Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url = 'http://www.jepx.org/market/excel/spot_2017.csv'\n",
    "spot_df = pd.read_csv(url, encoding='shift-jis', parse_dates=[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### インデックスの整形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T04:23:48.615087Z",
     "start_time": "2018-05-11T04:23:48.372543Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>売り入札量(kWh)</th>\n",
       "      <th>買い入札量(kWh)</th>\n",
       "      <th>約定総量(kWh)</th>\n",
       "      <th>システムプライス(円/kWh)</th>\n",
       "      <th>エリアプライス北海道(円/kWh)</th>\n",
       "      <th>エリアプライス東北(円/kWh)</th>\n",
       "      <th>エリアプライス東京(円/kWh)</th>\n",
       "      <th>エリアプライス中部(円/kWh)</th>\n",
       "      <th>エリアプライス北陸(円/kWh)</th>\n",
       "      <th>エリアプライス関西(円/kWh)</th>\n",
       "      <th>...</th>\n",
       "      <th>回避可能原価全国値(円/kWh)</th>\n",
       "      <th>回避可能原価北海道(円/kWh)</th>\n",
       "      <th>回避可能原価東北(円/kWh)</th>\n",
       "      <th>回避可能原価東京(円/kWh)</th>\n",
       "      <th>回避可能原価中部(円/kWh)</th>\n",
       "      <th>回避可能原価北陸(円/kWh)</th>\n",
       "      <th>回避可能原価関西(円/kWh)</th>\n",
       "      <th>回避可能原価中国(円/kWh)</th>\n",
       "      <th>回避可能原価四国(円/kWh)</th>\n",
       "      <th>回避可能原価九州(円/kWh)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</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>2017-04-01 00:00:00</th>\n",
       "      <td>4812000</td>\n",
       "      <td>3765500</td>\n",
       "      <td>1319000</td>\n",
       "      <td>10.23</td>\n",
       "      <td>11.79</td>\n",
       "      <td>11.79</td>\n",
       "      <td>11.79</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.58</td>\n",
       "      <td>...</td>\n",
       "      <td>10.26</td>\n",
       "      <td>11.80</td>\n",
       "      <td>11.79</td>\n",
       "      <td>11.79</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.65</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.58</td>\n",
       "      <td>7.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01 00:30:00</th>\n",
       "      <td>5082000</td>\n",
       "      <td>3663500</td>\n",
       "      <td>1243500</td>\n",
       "      <td>9.62</td>\n",
       "      <td>10.34</td>\n",
       "      <td>10.34</td>\n",
       "      <td>10.34</td>\n",
       "      <td>8.64</td>\n",
       "      <td>8.64</td>\n",
       "      <td>8.64</td>\n",
       "      <td>...</td>\n",
       "      <td>9.63</td>\n",
       "      <td>10.34</td>\n",
       "      <td>10.29</td>\n",
       "      <td>10.31</td>\n",
       "      <td>8.70</td>\n",
       "      <td>8.74</td>\n",
       "      <td>8.69</td>\n",
       "      <td>8.64</td>\n",
       "      <td>8.64</td>\n",
       "      <td>6.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01 01:00:00</th>\n",
       "      <td>5717500</td>\n",
       "      <td>3594000</td>\n",
       "      <td>1186500</td>\n",
       "      <td>9.17</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.20</td>\n",
       "      <td>8.76</td>\n",
       "      <td>8.76</td>\n",
       "      <td>8.76</td>\n",
       "      <td>...</td>\n",
       "      <td>9.21</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.16</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.93</td>\n",
       "      <td>8.81</td>\n",
       "      <td>8.76</td>\n",
       "      <td>8.76</td>\n",
       "      <td>7.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01 01:30:00</th>\n",
       "      <td>5926000</td>\n",
       "      <td>3634000</td>\n",
       "      <td>1154500</td>\n",
       "      <td>8.80</td>\n",
       "      <td>10.03</td>\n",
       "      <td>10.03</td>\n",
       "      <td>10.03</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.77</td>\n",
       "      <td>...</td>\n",
       "      <td>8.87</td>\n",
       "      <td>10.03</td>\n",
       "      <td>10.04</td>\n",
       "      <td>10.00</td>\n",
       "      <td>8.80</td>\n",
       "      <td>8.95</td>\n",
       "      <td>8.82</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.77</td>\n",
       "      <td>7.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01 02:00:00</th>\n",
       "      <td>6164500</td>\n",
       "      <td>3682500</td>\n",
       "      <td>1158500</td>\n",
       "      <td>8.89</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.32</td>\n",
       "      <td>8.84</td>\n",
       "      <td>8.84</td>\n",
       "      <td>8.84</td>\n",
       "      <td>...</td>\n",
       "      <td>8.92</td>\n",
       "      <td>9.33</td>\n",
       "      <td>9.36</td>\n",
       "      <td>9.33</td>\n",
       "      <td>8.87</td>\n",
       "      <td>8.87</td>\n",
       "      <td>8.90</td>\n",
       "      <td>8.84</td>\n",
       "      <td>8.84</td>\n",
       "      <td>7.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01 02:30:00</th>\n",
       "      <td>6177000</td>\n",
       "      <td>3674500</td>\n",
       "      <td>1355000</td>\n",
       "      <td>8.69</td>\n",
       "      <td>9.01</td>\n",
       "      <td>8.69</td>\n",
       "      <td>8.69</td>\n",
       "      <td>8.69</td>\n",
       "      <td>8.69</td>\n",
       "      <td>8.69</td>\n",
       "      <td>...</td>\n",
       "      <td>8.72</td>\n",
       "      <td>9.01</td>\n",
       "      <td>8.70</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.72</td>\n",
       "      <td>8.75</td>\n",
       "      <td>8.75</td>\n",
       "      <td>8.69</td>\n",
       "      <td>8.69</td>\n",
       "      <td>7.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01 03:00:00</th>\n",
       "      <td>6334500</td>\n",
       "      <td>3658500</td>\n",
       "      <td>1414000</td>\n",
       "      <td>8.64</td>\n",
       "      <td>9.01</td>\n",
       "      <td>8.65</td>\n",
       "      <td>8.65</td>\n",
       "      <td>8.65</td>\n",
       "      <td>8.65</td>\n",
       "      <td>8.65</td>\n",
       "      <td>...</td>\n",
       "      <td>8.67</td>\n",
       "      <td>9.01</td>\n",
       "      <td>8.66</td>\n",
       "      <td>8.70</td>\n",
       "      <td>8.69</td>\n",
       "      <td>8.71</td>\n",
       "      <td>8.72</td>\n",
       "      <td>8.65</td>\n",
       "      <td>8.65</td>\n",
       "      <td>7.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     売り入札量(kWh)  買い入札量(kWh)  約定総量(kWh)  システムプライス(円/kWh)  \\\n",
       "Datetime                                                                  \n",
       "2017-04-01 00:00:00     4812000     3765500    1319000            10.23   \n",
       "2017-04-01 00:30:00     5082000     3663500    1243500             9.62   \n",
       "2017-04-01 01:00:00     5717500     3594000    1186500             9.17   \n",
       "2017-04-01 01:30:00     5926000     3634000    1154500             8.80   \n",
       "2017-04-01 02:00:00     6164500     3682500    1158500             8.89   \n",
       "2017-04-01 02:30:00     6177000     3674500    1355000             8.69   \n",
       "2017-04-01 03:00:00     6334500     3658500    1414000             8.64   \n",
       "\n",
       "                     エリアプライス北海道(円/kWh)  エリアプライス東北(円/kWh)  エリアプライス東京(円/kWh)  \\\n",
       "Datetime                                                                     \n",
       "2017-04-01 00:00:00              11.79             11.79             11.79   \n",
       "2017-04-01 00:30:00              10.34             10.34             10.34   \n",
       "2017-04-01 01:00:00              10.20             10.20             10.20   \n",
       "2017-04-01 01:30:00              10.03             10.03             10.03   \n",
       "2017-04-01 02:00:00               9.32              9.32              9.32   \n",
       "2017-04-01 02:30:00               9.01              8.69              8.69   \n",
       "2017-04-01 03:00:00               9.01              8.65              8.65   \n",
       "\n",
       "                     エリアプライス中部(円/kWh)  エリアプライス北陸(円/kWh)  エリアプライス関西(円/kWh)  \\\n",
       "Datetime                                                                    \n",
       "2017-04-01 00:00:00              8.58              8.58              8.58   \n",
       "2017-04-01 00:30:00              8.64              8.64              8.64   \n",
       "2017-04-01 01:00:00              8.76              8.76              8.76   \n",
       "2017-04-01 01:30:00              8.77              8.77              8.77   \n",
       "2017-04-01 02:00:00              8.84              8.84              8.84   \n",
       "2017-04-01 02:30:00              8.69              8.69              8.69   \n",
       "2017-04-01 03:00:00              8.65              8.65              8.65   \n",
       "\n",
       "                          ...         回避可能原価全国値(円/kWh)  回避可能原価北海道(円/kWh)  \\\n",
       "Datetime                  ...                                              \n",
       "2017-04-01 00:00:00       ...                    10.26             11.80   \n",
       "2017-04-01 00:30:00       ...                     9.63             10.34   \n",
       "2017-04-01 01:00:00       ...                     9.21             10.20   \n",
       "2017-04-01 01:30:00       ...                     8.87             10.03   \n",
       "2017-04-01 02:00:00       ...                     8.92              9.33   \n",
       "2017-04-01 02:30:00       ...                     8.72              9.01   \n",
       "2017-04-01 03:00:00       ...                     8.67              9.01   \n",
       "\n",
       "                     回避可能原価東北(円/kWh)  回避可能原価東京(円/kWh)  回避可能原価中部(円/kWh)  \\\n",
       "Datetime                                                                 \n",
       "2017-04-01 00:00:00            11.79            11.79             8.55   \n",
       "2017-04-01 00:30:00            10.29            10.31             8.70   \n",
       "2017-04-01 01:00:00            10.20            10.16             8.78   \n",
       "2017-04-01 01:30:00            10.04            10.00             8.80   \n",
       "2017-04-01 02:00:00             9.36             9.33             8.87   \n",
       "2017-04-01 02:30:00             8.70             8.73             8.72   \n",
       "2017-04-01 03:00:00             8.66             8.70             8.69   \n",
       "\n",
       "                     回避可能原価北陸(円/kWh)  回避可能原価関西(円/kWh)  回避可能原価中国(円/kWh)  \\\n",
       "Datetime                                                                 \n",
       "2017-04-01 00:00:00             8.58             8.65             8.58   \n",
       "2017-04-01 00:30:00             8.74             8.69             8.64   \n",
       "2017-04-01 01:00:00             8.93             8.81             8.76   \n",
       "2017-04-01 01:30:00             8.95             8.82             8.77   \n",
       "2017-04-01 02:00:00             8.87             8.90             8.84   \n",
       "2017-04-01 02:30:00             8.75             8.75             8.69   \n",
       "2017-04-01 03:00:00             8.71             8.72             8.65   \n",
       "\n",
       "                     回避可能原価四国(円/kWh)  回避可能原価九州(円/kWh)  \n",
       "Datetime                                               \n",
       "2017-04-01 00:00:00             8.58             7.12  \n",
       "2017-04-01 00:30:00             8.64             6.27  \n",
       "2017-04-01 01:00:00             8.76             7.13  \n",
       "2017-04-01 01:30:00             8.77             7.13  \n",
       "2017-04-01 02:00:00             8.84             7.12  \n",
       "2017-04-01 02:30:00             8.69             7.12  \n",
       "2017-04-01 03:00:00             8.65             7.12  \n",
       "\n",
       "[7 rows x 30 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm = spot_df.loc[:, '時刻コード']/2*60 - 30.0\n",
    "h_list = []\n",
    "for m in hm:\n",
    "    h_list.append(dt.timedelta(minutes=m))\n",
    "\n",
    "spot_df.index = pd.to_datetime(spot_df.loc[:, '年月日']) + h_list\n",
    "del spot_df['年月日']\n",
    "del spot_df['時刻コード']\n",
    "spot_df.index.name = 'Datetime'\n",
    "\n",
    "display(spot_df['2017-4-1 00:00:00':'2017-4-1 03:00:00'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 必要なカラムの抽出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T04:23:48.639245Z",
     "start_time": "2018-05-11T04:23:48.618174Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\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>E_RATE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-01 00:00:00</th>\n",
       "      <td>11.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01 00:30:00</th>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01 01:00:00</th>\n",
       "      <td>10.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01 01:30:00</th>\n",
       "      <td>10.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01 02:00:00</th>\n",
       "      <td>9.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01 02:30:00</th>\n",
       "      <td>8.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01 03:00:00</th>\n",
       "      <td>8.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     E_RATE\n",
       "Datetime                   \n",
       "2017-04-01 00:00:00   11.79\n",
       "2017-04-01 00:30:00   10.34\n",
       "2017-04-01 01:00:00   10.20\n",
       "2017-04-01 01:30:00   10.03\n",
       "2017-04-01 02:00:00    9.32\n",
       "2017-04-01 02:30:00    8.69\n",
       "2017-04-01 03:00:00    8.65"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_column = 'エリアプライス東京(円/kWh)'\n",
    "target_df = pd.DataFrame(spot_df.loc['2017-04-01':'2017-12-31', target_column].copy())\n",
    "target_df.indexes = ['Datetime']\n",
    "target_df.columns = ['E_RATE']\n",
    "\n",
    "display(target_df['2017-4-1 00:00:00':'2017-4-1 03:00:00'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### グラフ化\n",
    "#### 年間"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T04:23:48.959217Z",
     "start_time": "2018-05-11T04:23:48.642114Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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976TBb2YX7GTdznAU8KpqCKrid3gdGBxj/VOq2tv+fBJjeyAs3bSX16aujrt9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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "x_text = 'Time  h'\n",
    "y_text = 'Half-hourly price  JPY/kWh'\n",
    "plt.xlabel(x_text)\n",
    "plt.ylabel(y_text)\n",
    "\n",
    "ax.xaxis.set_minor_locator(mdates.MonthLocator())\n",
    "ax.format_xdata = mdates.DateFormatter('%m')\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "plt.plot(target_df)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 移動平均\n",
    "\n",
    "Rolling average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T04:23:49.170912Z",
     "start_time": "2018-05-11T04:23:48.961810Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Rk+WlJd5J51TZuKM6W9IIqbJtdw2Pj13GHef1SlgwpxvLT+4RaErhnkgH/a63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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "x_text = 'Time  h'\n",
    "y_text = 'Half-hourly price  JPY/kWh'\n",
    "plt.xlabel(x_text)\n",
    "plt.ylabel(y_text)\n",
    "\n",
    "ax.xaxis.set_minor_locator(mdates.MonthLocator())\n",
    "ax.format_xdata = mdates.DateFormatter('%m')\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "plt.plot(target_df['E_RATE'].rolling(window=24, min_periods=2).mean())\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 差分\n",
    "\n",
    "first order difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T04:23:49.480219Z",
     "start_time": "2018-05-11T04:23:49.173964Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gJxbPSERI1rOM8PBn/jwj2SLQwewIIjIKeBxYguUe7yAilxhjPvJb3P7OiKiV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\nz3+hSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "x_text = 'Time  h'\n",
    "y_text = 'Half-hourly price  JPY/kWh'\n",
    "plt.xlabel(x_text)\n",
    "plt.ylabel(y_text)\n",
    "\n",
    "ax.xaxis.set_minor_locator(mdates.MonthLocator())\n",
    "ax.format_xdata = mdates.DateFormatter('%m')\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "plt.plot(target_df['E_RATE'].diff())\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 自己相関\n",
    "\n",
    "Autocorrelation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T04:23:50.480999Z",
     "start_time": "2018-05-11T04:23:49.482926Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9430484073963505"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BNH5zXn311QDMmzeP1atD82pGAed7CZaZzRVXXEGv3Cxef/111q37aBLscUDP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\npFBAERGRpFBAERGRpPj/zx40AupOATwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ok2eueUFdeRv2jmLzofG6suNw8tGK2zTijpomXD8Qx06UJyC2Zn1WdVWNLCaK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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(target_df['E_RATE'].autocorr())\n",
    "\n",
    "pd.plotting.autocorrelation_plot(target_df['E_RATE'])\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "pd.plotting.autocorrelation_plot(target_df['E_RATE'])\n",
    "plt.xlim(0,5000)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T04:23:50.979857Z",
     "start_time": "2018-05-11T04:23:50.483464Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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w46G7iDTM5SufuZnWlc1c8oPxa43xe3XUhfbw7Sf3WgHDlJ2KCgpjdRRaUChv6UYcOfNf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\nSbCgYIwxJsGCgjHGmIT/D+TcNeKCkJ8nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(target_df['E_RATE'])\n",
    "plt.xlim(0,100)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 自己回帰和分移動平均モデル\n",
    "\n",
    "Autoregressive Integrated Moving Average (ARIMA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T04:24:26.824334Z",
     "start_time": "2018-05-11T04:23:50.981947Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARIMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>D.E_RATE</td>     <th>  No. Observations:  </th>    <td>13199</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>          <td>ARIMA(5, 1, 1)</td>  <th>  Log Likelihood     </th> <td>-20646.580</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>    <td>1.156</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Fri, 11 May 2018</td> <th>  AIC                </th>  <td>41309.160</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>13:24:20</td>     <th>  BIC                </th>  <td>41369.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>04-01-2017</td>    <th>  HQIC               </th>  <td>41329.160</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 12-31-2017</td>   <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>          <td>-2.919e-05</td> <td>    0.000</td> <td>   -0.075</td> <td> 0.940</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.D.E_RATE</th> <td>    0.9830</td> <td>    0.009</td> <td>  112.771</td> <td> 0.000</td> <td>    0.966</td> <td>    1.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.D.E_RATE</th> <td>   -0.0249</td> <td>    0.012</td> <td>   -2.043</td> <td> 0.041</td> <td>   -0.049</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3.D.E_RATE</th> <td>    0.0448</td> <td>    0.012</td> <td>    3.675</td> <td> 0.000</td> <td>    0.021</td> <td>    0.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L4.D.E_RATE</th> <td>   -0.0287</td> <td>    0.012</td> <td>   -2.354</td> <td> 0.019</td> <td>   -0.053</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L5.D.E_RATE</th> <td>   -0.0543</td> <td>    0.009</td> <td>   -6.232</td> <td> 0.000</td> <td>   -0.071</td> <td>   -0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.E_RATE</th> <td>   -0.9970</td> <td>    0.001</td> <td>-1400.813</td> <td> 0.000</td> <td>   -0.998</td> <td>   -0.996</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>           Real</th> <th>         Imaginary</th> <th>         Modulus</th> <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th>               1.1367                    -0.0000j                    1.1367                   -0.0000     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th>               1.5202                    -0.0000j                    1.5202                   -0.0000     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.3</th>              -0.3333                    -2.0298j                    2.0570                   -0.2759     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.4</th>              -0.3333                    +2.0298j                    2.0570                    0.2759     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.5</th>              -2.5188                    -0.0000j                    2.5188                   -0.5000     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th>               1.0030                    +0.0000j                    1.0030                    0.0000     \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             ARIMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:               D.E_RATE   No. Observations:                13199\n",
       "Model:                 ARIMA(5, 1, 1)   Log Likelihood              -20646.580\n",
       "Method:                       css-mle   S.D. of innovations              1.156\n",
       "Date:                Fri, 11 May 2018   AIC                          41309.160\n",
       "Time:                        13:24:20   BIC                          41369.063\n",
       "Sample:                    04-01-2017   HQIC                         41329.160\n",
       "                         - 12-31-2017                                         \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "const          -2.919e-05      0.000     -0.075      0.940      -0.001       0.001\n",
       "ar.L1.D.E_RATE     0.9830      0.009    112.771      0.000       0.966       1.000\n",
       "ar.L2.D.E_RATE    -0.0249      0.012     -2.043      0.041      -0.049      -0.001\n",
       "ar.L3.D.E_RATE     0.0448      0.012      3.675      0.000       0.021       0.069\n",
       "ar.L4.D.E_RATE    -0.0287      0.012     -2.354      0.019      -0.053      -0.005\n",
       "ar.L5.D.E_RATE    -0.0543      0.009     -6.232      0.000      -0.071      -0.037\n",
       "ma.L1.D.E_RATE    -0.9970      0.001  -1400.813      0.000      -0.998      -0.996\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                 Real           Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            1.1367           -0.0000j            1.1367           -0.0000\n",
       "AR.2            1.5202           -0.0000j            1.5202           -0.0000\n",
       "AR.3           -0.3333           -2.0298j            2.0570           -0.2759\n",
       "AR.4           -0.3333           +2.0298j            2.0570            0.2759\n",
       "AR.5           -2.5188           -0.0000j            2.5188           -0.5000\n",
       "MA.1            1.0030           +0.0000j            1.0030            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ar = 5 # Autoregressive model(AR)\n",
    "i = 1  # Integrated model (I)\n",
    "ma = 1 # Moving Average (MA)\n",
    "\n",
    "model_arima = ARIMA(target_df['E_RATE'], order=(ar, i, ma))\n",
    "model_fit = model_arima.fit(disp=0)\n",
    "display(model_fit.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T03:44:23.919861Z",
     "start_time": "2018-05-11T03:44:23.916478Z"
    }
   },
   "source": [
    "##### 残差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T04:24:28.413907Z",
     "start_time": "2018-05-11T04:24:26.829075Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Fi4xQ+LEsx6YoivPMHPOTsMd0adUw8gKzqGkfk+e+6yojFL7OtFWyjfyWDTiz\nT5tkixERx+c3r/rcrEEOeY3r+R1zcveWIcs4u/9RjsuVajTOcT/aMCMUfir78L0f9lTjJ71bJ1uE\nuLh3ZJ9ki5AUGtStTeFdp/PiVYP99rVuXI+bTusGwDEdmgYto1mDnKD7nKZt02o3z5y7zwDg1WtP\n8DnmlWtO8Dq+2oXTuon1cvjPFYNC1vHLEzsx/oaTQh4z8mj/l8bZXcJfh4m/OzXsMelCRij8U7vn\n0aGR8PB5/fj6j6eHPLb3UY0TJJXFFSd1ZvqdBXz++2Gc2qOVzz6PK/OW07vxxxG9ffY9cn6/iOuo\nVye223j9qV2qPodrLG7xzOXHxXxuo9zY5w2e2KUFz/8qtBJxi18XWAq5oFce3Vs34sKB7av25eaE\nv5ehOrTdWzfi0uOtqS+hnvXbvSJlNo4dVfX5wXP7MqRraGs7UrrarptrTs7nruG9uPbkLuTaETen\n9cyrOu7S4zuSU7sWfxjRizZN6lW5fC4c2N6vXQD8KUDbGHN2b4Z0Cy33kG6+7e/o9k25pFddnr18\nYMjzvF9AbpIR2TITQdMGOfz5lAZcNTQ/7LFvXH9i0H1XBzi/TRP/7mc0nHdsO7q0akjPNo15/Trf\nuju1aADARQM7cFJX38iFK4dUyzJrzE9CNt5v/3w2R7cPbM3V7GH85vTu1V8MTLvjNO4b1cevsdS1\nXyJXDenM+zcP4dgg1mIoKzIcbZvm0i6CQb5g1FwkOxraN6tP8xoLgXvj7WeO9xmoyeUnduKLO07j\nxSsHM/X203j84gFV+8b9yt9qj4T7z7FyErZrVp/8Vg1576YhIZcTHHl024Dbrzm5C+NHn+RnnHiY\nfNuwiGWqZyv3nNq1uOX07jxwrm/exO7NanHvyD6MvegYAH5d0J2595xJj9aNAHxeEB4Gd27Or4bk\nc9kJnaq2Fd5ZQOPc0M/Csod+6rcGssfgGnVMW/7vpiER/y63MAlQ+Rmh8APRtmku8+49g+eu8H17\nB2rkFwxoF7ScgZ2qFeY9I/2tjVAM6twcCTEL2LMv3G1u16x+wOiHhnVrs/pPIwB476Yhfl3Wdk1z\nefP6k/jfr4fSvEEOp/ZoxZ3DezHUVu4G6JbXiOtP7QrAS1cP5pbTu/HWDSdWCXX3yD4M6tyCZ7ys\noBH9nPGnDuzUPGDujUhxa+jm+lO6MP3OgqrvX951Ol/eVRD0+EC8cV1ww0JE6JrXiDq1/ZvfqT1a\n8fRloXs9dw3v5bftvGPb0aJh3ape2/H5LfyUpTeN6lm9o2h6h6seGUGvKHrI4YJx7jupPjcM6+q3\n/Z5RfXjr+hPpX8OIWfzAWVUK/889AAAeD0lEQVQG28Pn9WPq7aexcewo8iMYBG6cm+MXzffXC4+p\n+hwqVNRjWOTUdt7H3tprPEPiag2RkbEKv5aIHc4lNbZXf754cAfW/2Uk94zsw4h+R3HOMf5Wz0/7\nteGkri2YcdfpjB7WjY1jR/FmgF7C578fxhOXHOuz7a0bgjf6mgR6MbRtmsuJXYI/iHVq16pq1Lk5\ntWlcz9fKuamgG3Xr1OK4Ts1Z9MBPq3oYnqpqKsyf9G7DXcN7M7Rbqyprw5O2omOLBmwcO4ppd5zG\nv39ZrfzjSWthMLRpErq7HKj77iFc1Rv+OjL4zhDnesqdevswHr/4WHJzatOgbnTuo1OCWMiAn6Xp\nW7dw7rHBDRCAa0+pdsU9dG5fTu3RirzG9fjm/rPofVSToOd5Gyx17IbgsZSfu2IgYy88umr/eQFk\nqF83dBTJ69dZfvj6ObW5YEC7qusYbVBFvTq1Gdrd//o1a1C36nmvW6cW3e2eQKR46/v3bhpC33bB\nr5U3IsJr157AF3cURFVfJJzhNfCeCAvf7eRpKYe3Ym3dOJdatYTWTXJ57leDWLndf9X7RvVyeHt0\n4O7e0G4tmbVuDwA92zTmYEm5z/56dUI3kHCqcrY9wAWWwg1HzQcmXDsL9YCd1jOPqat2+Vlp3fJ8\nG1koK04I3XuprCSsS6d5w+Bul3BRKqF6V5Gc1711Y7q39rVoWzWqy4L7ziJ/zAREkr/4ztUnd+Hq\nk7uEPe79m4dyXMdmPPPFWhrVrqBO7Vqs/tMI6tq9jBH9fY2dXwzuSJ+2TcKuUevNqT0sv/wqu9d5\nbhTnJgLPrbp6aD7HRzj566ohnQEY5jXm4CTlCV6tL+Ms/Ia2JTa4yndt3eZBnZv7RQZEMf8jIjpF\noJQDEYnSGHN275DWbqByglmSHqs8VL3PXD6QL+8qCOh2cIq2zcIPhoWSsW6Mg9VO8H83DeHrP4aO\nOfe2mOPhooEd4i5jUOfm1KolLH1oOH851XpOc3NqUytEI4g3KWGwnqSbvH9zcF+8pz2E+10921Qb\nNeFEv+3MHgzqHHskXiiDxg0yTuE3b1iXybcN428XHeOzvVWjuj6RAUBA/yFAS6+bMKBjs4jrzmtc\nj5eGR6H0q5678C0iN6c2Z/UN7Tu/5PiOPt/DlRpqf25ObTq3DO8bjacth1PYR7dvmpR5N5GoucH5\nLcLOKr3Ua2AxErzLu9F+Nlc+Mpw/jPD32SeCeF0MnuuYyHkygzq3CBjnD9UvnnCGXqcW1c99ONFP\n7ZHHr07qHFauy08M/Cw0rpdYJ0vGKXyAXkc19huwCnTjgo3sez8wgR4eT8RMoKigaGyi0fZgaTg/\ntgd/l43v95oDT+GmJ4TyJTuBAf51yYDQB4TgV0M6uyZjg7q1w44BeNOwnvU8nXNMaP96rMy5+wwm\n3VYd7z3m7N6s+8tIGtStE9dQ3gPn9OWKk6J78XiI+9JHGJTgNPPvPdMnqu2Va44H4Cg7vLJzy8iN\nsnAvPZHIXowRRZQl4EJlnQ/fCVo2qlcVu3z32b15eeZGv2OGhokJBssCDGQFTrvjtIjkCPR81K1T\ni1J7yccGQQbZaiWwIV5wXHtue2dxAmqKjmtO7sLeQ0FWSgugYRvUrcOSB39aFd0Silgm2x1VI9Zb\nRHAiKMR7gDfRXHtyPr97ezFdIugpOs19o/pyxX+tRZEKelkTDM85pi3NGuRwSoABYYDPfnsqq3Yc\nYOLyHVXbPOMSwejUogGb9hwKecz5A9olfazHQ0Za+IFwa42UG0/rxpx7qgdXRYSptw/jhStji6cG\n/4FRD5E8NJ54/MFtavOLQYF9v1XXIo6HUCTwvAWnqVPLnUe0WQwx/E3r5/gl/vJEuZzd/6iqyXLB\nXGGe2PYmUdad17geo4O4H90kqpw3ATh/QHs2jh2VcD81BI6SEhFO7ZEXdDC/b7smXOTVZp68dADD\nw4QgN4wgeuvJS2OfXOg0WaPwE0n31o1pGKVvLpL3USR5wT0zNUfk54QdcI3HR7vhr6N46LzIZwMH\nJIIfPaL/UWHzrETLb8/oQctGwSdTRRMP7XnBXnBc+5Bx7wAPndeP/5zZgCZhJgn5ySPCPXYaiUT6\nfKN9hjMFj2EVSShupIZkIkIuIyHjFX6qdKVq8rLtV4yG5g3r+i4eEeC3PXHxAH53Rg+6NQt+ayOJ\n0omUiwd3DH+QTSxT1GvXEh44J84XSw08/l0nOn2XndCR928eGtYSBKvnVr9O7LW+dcOJfH575DNd\nldjwNItI71RE7SiCYzS1QgZzeq/YEpcN6BQ6aqh1k1x+f1bP0DN87f9OKPzLTujkk4vF2+PRp0Wc\nj5ctXzSzOyPBI+KxUURgBS1LJK6wvGgY2q2VTyIyRYmWjFf4mby++YldI185KBBuWBTHdKhWor8b\nmNoLV+QEcXll8jOjOE88k7K8n7XuzTQfftxEY8V6IjC6RTllO1k8fVnoLH/B8Phm67iQGyTfDnkb\n3Lk5uWHcF2F95S4p3nAKXfV9duMJBY70xd+qUT3m33tm6DLDlDHqmLac0cn9MZOMV/geIhmI69Sy\nAa9dewJ/rzFpK1UJl9skGH86vz93nNWT08KEnMWCZ27DqAB5iaLGJadmOCNALfzspsqHH8VzEOug\n7M8HdaRLq4bcfXbvmFOBREN2DsOHwK2cGalE0wY53HpGD1fKDvXMpuoAek2u8kpNrWQvTmavDDaB\n8KimuVWZWdc6VltwMt7C/0mf1lw0sEP8IYQuo1ZlAJLk0mkd4cznbOXt0SdxXJjggVTgN6d3p3EM\ni+REY5hEOqclVYydjFf49erU5rGLj/Wbyagknqhfal6N5J+/ODb4cXHw6a2ncJRDCt6zUlSwCW+Z\nwkldW/L+TUN57oqBzrjuXOLO4b1Y9tDwqM/zzMSNJEOth3jyViWSjFf4SmYQy8zY4FS/efq3b+oz\nUzoePGsGnOjQEoGpTK1awoj+bePOqJmKXH9qF+bcfUbU+fbTAdcUvog8JCLbRGSx/RdiNQolWoLl\nA0kVAqmBqLu1SdAlTqfMznQy8XKJSNQegbBrT6SIie+2hf+EMWaA/feZy3VlBZ6c+wW90n9wuaZx\nWKeWMPF3p0Z0bE2uiyJJWKiyFj/404jLUZRIB3YTtRB6ODRKJwjvjD4pJQfvOrdsyDf3n0XzBk66\nOFKHPm0jW3auJj0i6H43a5DDj8VlIY+JNs+Nkp1Eu9rZtad0YdKKnSzctM89oSLAbYX/GxG5ElgA\n3GGM8fu1IjIaGA2Ql5dHYWFhTBUVFRVFfW644zfZf27IUfOYjfsrIpbLSTmcZtu2bQCsWbuWVi2P\n+Mhw5MgRn2M3b9pMYeHOqu/GGJ/jv129msKidQAs2+W7fCT4Xqdvv/025H6ALo0qWVQMK5Yvp97u\n1QHld/OaFRYWJu2+eBOrDIHO2bWrJOR+N+RwkphlsJX9jBlfUqeWsK8k+FKFnvJ75JaxMMi+uGSJ\ngrgUvohMBQJljboX+A/wJ6xL8yfgMeDamgcaY8YB4wB69eplCgoKYpKlsLCQiM+dNAEg8uOdlCNI\n3cu27ofZXzsmV1TXwwE+6LqPjxdvt1Y32ryJHt2706hskyWD/Zvr1asHR6oVROfOnSgo6F21v1Yt\n8Tm+V+/eFNjJ2SpXfw/fLPCp0+fYXr1gxbKg+wFatmwFu76nf//+FNRMdubWM2GXO/eeM2jTJDfh\n9yUQsbQVCHxtPtixCHZsD7rfMTlcIloZpvYtorS8klFPfwUGTjvtNHJq12LH/sNQ+EXAczzlr629\nHr5dFXBfLLLEQlwK3xgTej6xjYi8AHwaT12ZQvtm9dn24+Fki+E4Azs1Z2Cn5rwycwMA7Zs3gF2+\nx4SbjRjPwFZkpyZv5CzSVc3SjQwM0glJsMidVBmUDYdrLh0RaWuM8Swd8zNguVt1pROf3HoK2wMo\n/PbNrSyID57bN9EiOcpVQ/Pp07YJJ3ZtSeGuVSGPdaKNDOjYjMVbfozqnERMYVcym3RR8DVx04f/\ndxEZgNWuNwI3ulhXVDxyfj+OlAX3ublJi4Z1aRFgBaAWDev6pBlOV0TElTj0od0Ch6G+e+MQyisr\n+Wjx9rBlpGsjVVIXBxaPSyiuKXxjzK/cKjtertRcKSlBTTvbr9F4bQi2mlTdOrWoq/MHk0a29pWi\njdJJFbSlKIqixEmw5Giphip8JWGkykzbbLVKFeeo+Synib5Xha+4z6XHd2RggOyKNcdO/aykKBpR\nJA0uTdqkkkakWwCAzrRVXGesvaDMCY9OTbIkFmnWRlOadFN42Y5a+ErqorokZVhwX0RTbpQURxW+\nktFM+f2wqs/pMrCWSoy/4SQ+vOVkWjWql2xRUpJ0s0nUpaNkBMFm8fZo09hvWyK9EP+9ajC10jjn\n8pBuoedUpO8vc5Z0sSVU4SsZyYUD2/ttS0abPKNPmyTUqiiBUZeOknU4uTi1okD4PFGpgip8JWnU\nVLzxNJmIwjLTo00qaUS6BSmpwlcSRsro2zRrpCmNXksgfYwJVfhK2nDfqD68fPXxER3764Juftta\nNrKS1uXWCZyXR1FiJRZ9//7NQx2XIxw6aKukDdef2jXovpoNrntr/+icR87vz8BOzTmpawuHJVOy\nlVgnnrVpUo9BnZs7LE14VOErScM/tYK79TWqV4crTursbiVKVhLtHI9kBQ6oS0dRlJjRiKf0Qi18\nJWFEbcGnyUCYkn18euspTF6xs+p7ujyqqvCVpHH5iZ18vl94nP9kKUVJRfq3b0r/9k2TLUbUqMJX\nEs6Np3VlzIjefgNef//5Mb4HJtBbMP6Gk2jfrH7iKswQ6tdVrzBA11YNue6ULvz36w0RHZ+s+H29\nW0rCue6ULgGjG+rUjuNxjHPEd0i3lnRq2SCuMrKRu4b3TrYIKYGIcP85fZMtRlhU4SspQbumuckW\nQYmBxvXUSZBO6N1SEkhgK3zq7cMcTb97bEf/1bUUJZVIVmyTKnwl4dQM5Qs0SSpavF8lGiiYONIt\nl0y2oy4dJeGkS2ZBRXGLZLUAVfhKAlFzUFGSSVwKX0R+ISIrRKRSRAbX2He3iKwVkW9FZHh8YirZ\nxM8HdYj6HO8gHe0/KKmI92I4yTJ94rXwlwMXAjO8N4pIX+BSoB8wAvi3iGiKwqwnMlXsGcBtkqtD\nTKlOrMnDspEurRoyc8xPkipDXC3KGLMKAt7084G3jTFHgA0ishY4AZgdT31KZhAu/8rvz+pBt7yG\nDO93VIIkUpTswC0Tqj0wx+v7VnubHyIyGhgNkJeXR2FhYUwVFhUVxXyuk6gcwWUoLS0FYNasWTSt\nF1rp5wFffrku5DHeZX+3qazqc92ygwF/e7KvB6TmfXGKaMvM5GvhjXf5PxyuBKDkyBG/ehMhS1iF\nLyJTgUCm1r3GmI+CnRZgW8D+vDFmHDAOoFevXqagoCCcSAEpLCwk1nOdROUILkPdr6dAaSlDhw4l\nr3EccfeTJgD4lL1x5gZYtZI2Terx8s0FNPKeEBTg+GSRivclbmK8vpl4Lf7VdBu3vbPYZ5t3+Vv3\nFcOX06mfm+tXbyKuR1iFb4w5M4ZytwIdvb53ALbHUI6SQSRiGbgR/Y7yVfaKkkAuOK69n8JPJdwK\ny/wYuFRE6olIF6AHMM+lupQ0w41xPs/s2qHdWzlfuKI4RLLXvo3LFBKRnwFPY7lcJ4jIYmPMcGPM\nChF5F1gJlAO3GGMq4hdXUQJzXKfmLHvopzTOzUm2KIqSssQbpfM/4H9B9j0KPBpP+YoSDarsFSU0\nOtNWURQlS1CFryQMnQGrKMlFFb6ScHRuppLt6IpXiqIoiquowlcURckSdIaKoiiKC9x4Wlc2/VDs\nsy2t4/AVJRpGHd2W1+dson5dTZyaafxhRK9ki5By3H12n6D7kuXDV4WvJIyHzuvH7Wf1pEFdfewy\niY1jRyVbBCVCtOUpCaN2LaF5w7pxlzPht6dE1TV+4KRc6rTpFne9ihIJE393Ks0aBJ4EmOzlPVXh\nK2lHv3ZNozq+a7PaFAzJd0cYRalBn7ZNwh4Tbk0It9AoHUVRlCxBFb6iKEqWoApfURQlQXjWa756\naH5S6lcfvqIoSoJoWK9OUqOa1MJXFEXJElThK4qiZAmq8BVFUbIEVfiKoihZgip8RVGULEEVvqIo\nSpagCl9RFCVLUIWvKIqSJYhJdkZ+L0TkIPAt0BTYH+LQQPtbAT+E2B/qXCf3e8uR6Lq999eUI5F1\ne/DIkIy6vfflkJx7Es9z6kbd3jKUJalu7/3B2koi6vbgZDtxU3eEO7+XMaZxiHMtjDEp8wcssP+P\nC3Oc337PueHOj6XsKPcvSGLd44LJkci6nbifTu0HxiXrnsTznLpRt7cMyarbe3+w+5KIugPdj0TX\nHerZiOWehjrX85eqLp1PXNzvZtnhcLvuZP5urVvr1rrjKzsc8Z6fci6dBcaYwYk+10lUjtSSIZXk\ngNSQJRVkSBU5UkEGD4nQf6lm4Y9L0rlOonJUkwoyQOrIAakhSyrIAKkhRyrI4MF1/ZdSFr6iKIri\nHqlm4SuKoiguoQpfURQlS0g7hS8iPxMRIyK9E1yvEZHXvb7XEZHdIvJpIuUIIFdRMuv3JpwsIlIo\nIq4MkCXruQggx70iskJElorIYhE5MUlydBCRj0RkjYisE5EnRaRuiONvE5EGDstgROQxr+93ishD\nTtYRgQwV9n1YISJLROR2EUmq3ktmm007hQ9cBnwNXBrNSSJSO856DwH9RaS+/f0sYFucZSrOEdNz\n4SQiMgQ4BxhojDkGOBPYkgQ5BPgA+NAY0wPoCTQCHg1x2m2AowofOAJcKCKtHC43Gg4bYwYYY/ph\ntdmRwINJlCeppJXCF5FGwMnAddgNW0QKRGSGiPxPRFaKyHOeN7iIFInIIyIyFxjigAgTAc/6ZJcB\n471kO0FEZonIIvt/L3v7VyIywOu4mSJyjAOyVGFfg0+9vj8jIlfbnzeKyMMi8o2ILHPbAg4li4t1\nBnsugl2TkSKyWkS+FpGnHOyltQV+MMYcATDG/GCM2S4ig0TkSxFZKCKTRaStLUehiPzLfl6Wi8gJ\nDsnxE6DEGPOyLUcF8HvgWhFpKCL/tJ+FpSJyq4j8FmgHTBeR6Q7JAFCOFT3y+5o7RKSziEyzZZgm\nIp1EpKn9vHrabwMR2SIiOU4IY4zZBYwGfiMWtUXkHyIy35bjRi/5/mBfoyUiMtaJ+r0RkUb27/a0\ny/Pt7fkiskpEXrB7JZ97GZlxk1YKH7gAmGSM+Q7YKyID7e0nAHcARwPdgAvt7Q2B5caYE40xXztQ\n/9vApSKSCxwDzPXatxoYZow5DngA+Iu9/UXgagAR6QnUM8YsdUCWaPjBGDMQ+A9wZ4LrTgTBngs/\n7Hv3PHC2MeYUIM9BOT4HOorIdyLybxE5zVZWTwM/N8YMAl7C19JuaIwZCvza3ucE/YCF3huMMQeA\nzcD1QBfgOLsX8qYx5ilgO3C6MeZ0h2Tw8CzwSxFpWmP7M8BrHhmAp4wx+4ElwGn2MecCk40xZU4J\nY4xZj6X3WmMZCPuNMccDxwM3iEgXETkb65k60RhzLPB3p+r3ogT4md0uTwces3tmAD2AZ+1eyY/A\nRU5Vmm4K/zIspYv9/zL78zxjzHrbkhkPnGJvrwDed6pyW1Hn2/V+VmN3U+A9EVkOPIHV6ADeA86x\nG/61wCtOyRMFH9j/F2LJn2kEey4C0RtYb4zZYH8fH+LYqDDGFAGDsKzI3cA7wI1Af2CKiCwG7gM6\neJ023j53BtBERJo5IIoAgeKtBRgGPGeMKbfr3etAfUGxXzSvAb+tsWsI8Jb9+XWq2+w7wCX250vt\n707jUaw/Ba6078tcoCWWsj0TeNkYU2z/BjeukQB/EZGlwFSgPdDG3rfBGLPY/uxom63jVEFuIyIt\nsbqq/UXEALWxHurP8H+4Pd9L7JeAk3wM/BMowHpAPPwJmG6M+ZmI5AOFAMaYYhGZApwPXAy4MWhZ\nju/LO7fG/iP2/wrcv+fhZHGUEM/Fx0HkEFzEft4KgUIRWQbcAqwwxgRzKQZ7duNhBTWsQhFpAnQE\n1jtURzT8C/gGeDnEMR6ZPgb+KiItsF6eXzgpiIh0xWoHu7CehVuNMZNrHDMC96/RL7F6l4OMMWUi\nspHqZ/SI13EVQFa6dH6O1QXsbIzJN8Z0BDZgWQYn2F2xWljWgRPum2C8BDxijFlWY3tTqgdxr66x\n70XgKWC+S9bCJqCviNSzu85nuFBHqsoS7LkgiByrga72Sxmqrcm4EZFeItLDa9MAYBWQJ9aALiKS\nIyL9vI65xN5+CpZ7IVQ2xUiZBjQQkSvtsmsDj2H1Lj8HbhKROva+FvY5B4Hw2RZjwH7m38VyoXiY\nRfUA+y+x26zdS5oHPAl86qTBJiJ5wHPAM8aacToZuNkzRiAiPUWkIdY1ulbsqCWva+QkTYFdtrI/\nHejsQh1+pI2Fj9VNrzl48j5wMzDb3nc0MAP4n1tCGGO2Yj2MNfk78KqI3E4Nq8QYs1BEDhDawoka\nu9EeMcZsEZF3gaXAGmCRk/WkuCzBnovLsZSMjxzGmMMi8mtgkoj8gKVcnKIR8LTtlikH1mK5d8YB\nT9kvnjpYFu8K+5x9IjILaILl8osbY4wRkZ8B/xaR+7EMu8+Ae7Asxp7AUhEpA17A8qePAyaKyA4X\n/PhgvXB+4/X9t8BLInIXlvvrGq9972C5QgscqLe+7bLJwbonrwOP2/texHKXfGP7z3cDFxhjJokV\naLFAREqpvnZx42knWOMWn4jIAmAxliHiOmmfWkFECoA7jTHnJFuWYIhIO6xufm9jTKWD5R4LvGCM\ncSq6IyNkCYeINDLGFNmN/FlgjTHmiSTIUYj17C5IdN1Kckh2O0knl05aYner5wL3Oqzsb8Ia8LvP\nqTIzQZYIucG2+lZgda2fT7I8ShaQCu0k7S18RVEUJTLUwlcURckSVOEriqK4gIh0FJHp9szZFSLy\nO3t7CxGZIlaeoyki0tze3ltEZovIERG5s0ZZv7fLWC4i4+0JhFGjCl9RFMUdyoE7jDF9gJOAW0Sk\nLzAGmGbnOZpmfwfYixW99E/vQkSkvb19sDGmP9Zck5hyRqnCVxRFcQFjzA5jzDf254NYczLaY03C\nfNU+7FWsNA4YY3YZY+YDgVJJ1MEKMa2DleRueywyqcJXFEVxGXui33FYEXttjDE7wHopYOX1CYox\nZhuW1b8Z2IE1Qe/zWORQha8oiuIiYmVzfR+4zc4tFO35zbF6BV2wspo2FJErYpFFFb6iKIpL2Gkb\n3sfKSupJYvi9VKfIbouV1ycUZ2IlVNttZw79ABgaizyq8BVFUVzAnsn9X2CVMeZxr10fA1fZn68C\nPgpT1GbgJLHWBxCsvFCrYpJJJ14piqI4j50Q7ytgGeCZZX8Plh//XaATljL/hTFmr4gcBSzAyqtU\nCRQBfY0xB0TkYaxEe+VYeaGu9yy0E5VMqvAVRVGyA3XpKIqiZAmq8BVFUbIEVfiKoihZgip8RVGU\nLEEVvqIoSpagCl/JKESkQkQW25kFl4jI7fZax6HOyReRyyMo2+c4ERksIk85IbeiJAJV+EqmcdgY\nM8AY0w84CxgJPBjmnHysNXDD4XOcMWaBMea3McqpKAlH4/CVjEJEiowxjby+dwXmA62AzliLWDe0\nd//GGDNLROYAfYANWNkLn8JaGL0AqAc8a4x5PsBxi7DXUxaRh7BynbTFWiT8dqyUuGcD24BzjTFl\nIjIIaxHtRsAPwNWeRFqK4jZq4SsZjTFmPdZz3horZ8lZxpiBWLMWPe6YMcBXds/gCeA6rIyExwPH\nY62B2yXAcTXpBozCSnT1BjDdGHM0cBgYZedVeRr4uTFmEPAS8KgrP1xRAlAn2QIoSgIQ+38O8IyI\nDAAqsCzxQPwUOEZEfm5/bwr0AErD1DPRtuKXYS1SMcnevgzLHdQL6A9MsVKiUBsr3a2iJARV+EpG\nY7t0KrCs+weB74Fjsaz+kmCnAbcaYybXKKsgTHVHAIwxlSJSZqr9pZVYbU2AFcaYITH8FEWJG3Xp\nKBmLiOQBzwHP2Mq3KbDDGFMJ/ArLwgY4CDT2OnUycLPtgkFEeopIwwDHRcu3QJ6IDLHLzRGRfnGU\npyhRoRa+kmnUF5HFWO6bcqxBWk9q2n8D74vIL4DpwCF7+1KgXESWAK8AT2K5YL6x09HuxlqGruZx\ni6IRzBhTaruJnhKRpljt71/Aiph+qaJEiUbpKIqiZAnq0lEURckSVOEriqJkCarwFUVRsgRV+Iqi\nKFmCKnxFUZQsQRW+oihKlqAKX1EUJUv4fzBMaNMQLKSZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13199.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.000082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.156389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-12.765292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.224910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.058646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.140634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.109040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "count  13199.000000\n",
       "mean      -0.000082\n",
       "std        1.156389\n",
       "min      -12.765292\n",
       "25%       -0.224910\n",
       "50%       -0.058646\n",
       "75%        0.140634\n",
       "max       17.109040"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuals = pd.DataFrame(model_fit.resid)\n",
    "residuals.plot()\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "residuals.plot(kind='kde')\n",
    "plt.grid()\n",
    "plt.show()\n",
    "display(residuals.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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