arima_prediction.ipynb · OraYang/ARMA_prediction

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arima_prediction.ipynb 136.43 KB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##load data\n",
    "import prepare\n",
    "import importlib\n",
    "path = 'data/business.xls'\n",
    "business = prepare.loadDataSet(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test = pd.read_excel(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户编号</th>\n",
       "      <th>车辆编号</th>\n",
       "      <th>开始时间</th>\n",
       "      <th>结束时间</th>\n",
       "      <th>花费金额</th>\n",
       "      <th>实际支付金额</th>\n",
       "      <th>优惠金额</th>\n",
       "      <th>开始纬度</th>\n",
       "      <th>开始经度</th>\n",
       "      <th>结束纬度</th>\n",
       "      <th>结束经度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10043</td>\n",
       "      <td>90</td>\n",
       "      <td>9/28/2015 13:03:20</td>\n",
       "      <td>9/28/2015 13:36:04</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.825016</td>\n",
       "      <td>119.998132</td>\n",
       "      <td>31.825208</td>\n",
       "      <td>119.998005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10041</td>\n",
       "      <td>89</td>\n",
       "      <td>9/28/2015 12:48:27</td>\n",
       "      <td>9/28/2015 13:59:40</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.825028</td>\n",
       "      <td>119.998074</td>\n",
       "      <td>31.825097</td>\n",
       "      <td>119.998017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10046</td>\n",
       "      <td>79</td>\n",
       "      <td>9/28/2015 13:38:11</td>\n",
       "      <td>9/28/2015 14:48:32</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.824956</td>\n",
       "      <td>119.997859</td>\n",
       "      <td>31.825067</td>\n",
       "      <td>119.997897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10059</td>\n",
       "      <td>86</td>\n",
       "      <td>9/28/2015 14:47:54</td>\n",
       "      <td>9/28/2015 15:13:21</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.825093</td>\n",
       "      <td>119.998124</td>\n",
       "      <td>31.825201</td>\n",
       "      <td>119.998015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>726</td>\n",
       "      <td>86</td>\n",
       "      <td>9/29/2015 09:19:11</td>\n",
       "      <td>9/29/2015 09:19:17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.825090</td>\n",
       "      <td>119.997652</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    用户编号  车辆编号                开始时间                结束时间  花费金额  实际支付金额  优惠金额  \\\n",
       "0  10043    90  9/28/2015 13:03:20  9/28/2015 13:36:04    80       0     0   \n",
       "1  10041    89  9/28/2015 12:48:27  9/28/2015 13:59:40   160       0     0   \n",
       "2  10046    79  9/28/2015 13:38:11  9/28/2015 14:48:32   160       0     0   \n",
       "3  10059    86  9/28/2015 14:47:54  9/28/2015 15:13:21    60       0     0   \n",
       "4    726    86  9/29/2015 09:19:11  9/29/2015 09:19:17     1       0     0   \n",
       "\n",
       "        开始纬度        开始经度       结束纬度        结束经度  \n",
       "0  31.825016  119.998132  31.825208  119.998005  \n",
       "1  31.825028  119.998074  31.825097  119.998017  \n",
       "2  31.824956  119.997859  31.825067  119.997897  \n",
       "3  31.825093  119.998124  31.825201  119.998015  \n",
       "4  31.825090  119.997652   0.000000    0.000000  "
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>开始时间</th>\n",
       "      <th>rentNumber</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>2017-10</td>\n",
       "      <td>4</td>\n",
       "      <td>星期二</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>2017-10</td>\n",
       "      <td>11</td>\n",
       "      <td>星期三</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>2017-10</td>\n",
       "      <td>1</td>\n",
       "      <td>星期四</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>2017-10</td>\n",
       "      <td>9</td>\n",
       "      <td>星期五</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>2017-10</td>\n",
       "      <td>21</td>\n",
       "      <td>星期六</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>2017-10</td>\n",
       "      <td>17</td>\n",
       "      <td>星期天</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>2017-10</td>\n",
       "      <td>1</td>\n",
       "      <td>星期一</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        开始时间  rentNumber week\n",
       "711  2017-10           4  星期二\n",
       "712  2017-10          11  星期三\n",
       "713  2017-10           1  星期四\n",
       "714  2017-10           9  星期五\n",
       "715  2017-10          21  星期六\n",
       "716  2017-10          17  星期天\n",
       "717  2017-10           1  星期一"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business.tail(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "##get daylist\n",
    "importlib.reload(prepare)\n",
    "datelist = prepare.get_datelist('2015-09-28','2017-10-16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(datelist)-len(business)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           开始时间  rentNumber week\n",
      "85   2015-12-22         0.0    0\n",
      "117  2016-01-23         0.0    0\n",
      "118  2016-01-24         0.0    0\n",
      "126  2016-02-01         0.0    0\n",
      "132  2016-02-07         0.0    0\n",
      "147  2016-02-22         0.0    0\n",
      "156  2016-03-02         0.0    0\n",
      "191  2016-04-06         0.0    0\n",
      "268  2016-06-22         0.0    0\n",
      "389  2016-10-21         0.0    0\n",
      "394  2016-10-26         0.0    0\n",
      "395  2016-10-27         0.0    0\n",
      "408  2016-11-09         0.0    0\n",
      "409  2016-11-10         0.0    0\n",
      "414  2016-11-15         0.0    0\n",
      "416  2016-11-17         0.0    0\n",
      "420  2016-11-21         0.0    0\n",
      "421  2016-11-22         0.0    0\n",
      "422  2016-11-23         0.0    0\n",
      "423  2016-11-24         0.0    0\n",
      "436  2016-12-07         0.0    0\n",
      "450  2016-12-21         0.0    0\n",
      "451  2016-12-22         0.0    0\n",
      "455  2016-12-26         0.0    0\n",
      "465  2017-01-05         0.0    0\n",
      "472  2017-01-12         0.0    0\n",
      "511  2017-02-20         0.0    0\n",
      "513  2017-02-22         0.0    0\n",
      "541  2017-03-22         0.0    0\n",
      "603  2017-05-23         0.0    0\n",
      "621  2017-06-10         0.0    0\n",
      "728  2017-09-25         0.0    0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "new_business = pd.merge(datelist,business,how='outer')\n",
    "new_business.fillna(0,inplace = 'true')\n",
    "print(new_business[new_business['rentNumber']==0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>开始时间</th>\n",
       "      <th>rentNumber</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2015-12-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2016-01-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2016-01-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2016-02-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2016-02-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>2016-03-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>2016-04-06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>2016-06-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>2016-10-21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>2016-10-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>2016-10-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>2016-11-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>2016-11-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>2016-11-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>2016-11-17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>2016-11-21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>2016-11-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>2016-11-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>2016-11-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>2016-12-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>2016-12-21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>2016-12-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>2016-12-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>2017-01-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>2017-02-20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>2017-02-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>2017-05-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>2017-06-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>2017-09-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           开始时间  rentNumber week\n",
       "85   2015-12-22         0.0    0\n",
       "117  2016-01-23         0.0    0\n",
       "118  2016-01-24         0.0    0\n",
       "126  2016-02-01         0.0    0\n",
       "132  2016-02-07         0.0    0\n",
       "147  2016-02-22         0.0    0\n",
       "156  2016-03-02         0.0    0\n",
       "191  2016-04-06         0.0    0\n",
       "268  2016-06-22         0.0    0\n",
       "389  2016-10-21         0.0    0\n",
       "394  2016-10-26         0.0    0\n",
       "395  2016-10-27         0.0    0\n",
       "408  2016-11-09         0.0    0\n",
       "409  2016-11-10         0.0    0\n",
       "414  2016-11-15         0.0    0\n",
       "416  2016-11-17         0.0    0\n",
       "420  2016-11-21         0.0    0\n",
       "421  2016-11-22         0.0    0\n",
       "422  2016-11-23         0.0    0\n",
       "423  2016-11-24         0.0    0\n",
       "436  2016-12-07         0.0    0\n",
       "450  2016-12-21         0.0    0\n",
       "451  2016-12-22         0.0    0\n",
       "455  2016-12-26         0.0    0\n",
       "465  2017-01-05         0.0    0\n",
       "472  2017-01-12         0.0    0\n",
       "511  2017-02-20         0.0    0\n",
       "513  2017-02-22         0.0    0\n",
       "541  2017-03-22         0.0    0\n",
       "603  2017-05-23         0.0    0\n",
       "621  2017-06-10         0.0    0\n",
       "728  2017-09-25         0.0    0"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_business[new_business['rentNumber']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vlSNJkiRpEo6ckKbOnldJkiRJ0uDZ8ypJ2iF7ByRJ0pBYvEqSJEmSJhjahWyHDUuSJEmS\nBs/iVZIkSZI0eA4bliRJmiFDG2InSXOJPa+SJEmSpMGzeJUkSZIkDZ7FqyRJkiRp8CxeJUmSJEmD\nZ/EqSZIkSRo8i1dJkiRJ0uBZvEqSJEmSBs/3eZX2Qb4PoSTNbf6dlzQX2fMqSZIkSRq8afW8JtkE\nfBN4EtheVUuTHAZ8ClgEbAJeX1XfaNufB7ylbf/LVXXddM4v7S6vQEuSJEl7t5noef3PVbWkqpa2\n5yuBG6tqMXBje06S44DlwPHAMuCiJPvNwPklSZIkSXPcnhg2fCrwsbb8MeC0kfYrq+qJqnoQ2Aic\nuAfOL0mSJEmaY6ZbvBZwQ5KvJFnR2g6vqq1t+evA4W15AfDQyL6bW5skSZIkSbs03dmGf7SqtiR5\nAXB9kntHV1ZVJalnetBWCK8AWLhw4TQjSpIkSZL2dtPqea2qLe3jI8Bn6IYBP5zkCID28ZG2+Rbg\nqJHdj2xtOzrumqpaWlVL58+fP52IkiRJkqQ5YMo9r0meAzyrqr7Zln8SeA+wDjgTWN0+Xt12WQd8\nMskHgBcBi4FbppFdA+cMv5IkSZJmynSGDR8OfCbJ2HE+WVWfT3IrsDbJW4CvAa8HqKq7kqwF7ga2\nA+dU1ZPTSi9JkiRJ2idMuXitqgeAH9hB+6PAK3eyzypg1VTPKUmSJEnaN013wiZJkiRJe5mZuL0L\nvMVrT/B7s3MWr5Ik7QZfTEiSNLum+z6vkiRJkiTtcRavkiRJkqTBs3iVJEmSJA2exaskSZIkafAs\nXiVJkiRJg2fxKkmSJEkaPItXSZIkSdLgWbxKkiRJkgbP4lWSJEmSNHgWr5IkSZKkwbN4lSRJkiQN\n3v6zHUAzb9HKa6Z9jE2rT5mBJJIkSZI0M+x5lSRJkiQNnsWrJEmSJGnwLF4lSZIkSYM35eI1yVFJ\nvpjk7iR3JXl7a393ki1Jbm+P14zsc16SjUnuS/KqmfgEJEmSJElz33QmbNoO/GpV3Zbku4GvJLm+\nrftgVb1/dOMkxwHLgeOBFwE3JDm2qp6cRgZJkiQN1ExMIglOJCmpM+We16raWlW3teVvAvcAC3ax\ny6nAlVX1RFU9CGwETpzq+SVJkiRJ+44Zuec1ySLgZcDNrencJHckuTTJoa1tAfDQyG6b2XWxK0mS\nJEkSMAPFa5LnAlcB76iqx4GLgWOAJcBW4IIpHHNFkvVJ1m/btm26ESVJkiRJe7lpFa9JDqArXD9R\nVX8KUFUPV9WTVfUU8FG+MzR4C3DUyO5HtrYJqmpNVS2tqqXz58+fTkRJkiRJ0hwwndmGA1wC3FNV\nHxhpP2Jks9cBd7bldcDyJAcmORpYDNwy1fNLkiRJkvYd05lt+EeANwIbktze2n4TOD3JEqCATcBb\nAarqriRrgbvpZio+x5mGJUmSJEm7Y8rFa1X9FZAdrPrcLvZZBaya6jklSZIkSfum6fS8SpIkzTrf\nS1SS9g0z8lY5kiRJkiTtSRavkiRJkqTBs3iVJEmSJA2e97xKkqQpmYl7Tb3PVJK0u+x5lSRJkiQN\nnsWrJEmSJGnwLF4lSZIkSYNn8SpJkiRJGjyLV0mSJEnS4Fm8SpIkSZIGz+JVkiRJkjR4Fq+SJEmS\npMGzeJUkSZIkDZ7FqyRJkiRp8CxeJUmSJEmDt/9sB5iuRSuvmfYxNq0+ZQaSSJIkSZL2lN57XpMs\nS3Jfko1JVvZ9fkmSJEnS3qfX4jXJfsAfAK8GjgNOT3JcnxkkSZIkSXufvnteTwQ2VtUDVfUvwJXA\nqT1nkCRJkiTtZfouXhcAD40839zaJEmSJEnaqVRVfydLfhZYVlW/2J6/EfihqnrbuO1WACva0+8F\n7pvmqecB/zDNY8yEoeSA4WQxx0RDyWKOiYaSxRwTDSWLOSYaShZzTDSULOaYaChZzDHRULLMpRz/\nAFBVyybbsO/ZhrcAR408P7K1PU1VrQHWzNRJk6yvqqUzdby9PQcMJ4s5JhpKFnNMNJQs5phoKFnM\nMdFQsphjoqFkMcdEQ8lijomGkmVfzdH3sOFbgcVJjk7yXcByYF3PGSRJkiRJe5lee16ranuStwHX\nAfsBl1bVXX1mkCRJkiTtffoeNkxVfQ74XM+nnbEhyNM0lBwwnCzmmGgoWcwx0VCymGOioWQxx0RD\nyWKOiYaSxRwTDSWLOSYaSpZ9MkevEzZJkiRJkjQVfd/zKkmSJEnSM2bxKkmSJEkaPItXSZIkSdLg\n9T5h056W5PuAU4EFrWkLsK6q7pm9VLOrfU0WADdX1T+NtC+rqs/3nOVEoKrq1iTHAcuAe9tEXrMm\nyeVVdcYsZ/hR4ETgzqr6Qs/n/iHgnqp6PMlBwErgB4G7gd+pqn/sKccvA5+pqof6ON8ucoy9ldff\nV9UNSX4e+E/APcCaqvrXnvMcA/w03ftkPwn8LfDJqnq8zxySJGkYkrygqh6Z7Rx9m1M9r0neCVwJ\nBLilPQJckWTlbGYbleQXejzXLwNXA+cCdyY5dWT17/SVo2U5H7gQuDjJ7wK/DzwHWJnkt3rMsW7c\n48+Anx573mOOW0aWz6L7enw3cP4s/LxeCnyrLX8IOAR4b2v74x5z/DZwc5K/THJ2kvk9nnvUHwOn\nAG9P8nHg54Cbgf8I/FGfQdrv8EeAf9fOfyBdEXtTkpP7zCJNRZIXzHaGIUny/NnOoOFIckiS1Unu\nTfJYkkeT3NPanjfb+cYkubbHcx2c5HeTfLxdPB5dd1GPOV6Y5OIkf5Dk+UnenWRDkrVJjugrR8ty\n2LjH84Fbkhya5LAecywbWT4kySVJ7kjyySSH9xKiqubMg6434oAdtH8XcP9s5xvJ83c9nmsD8Ny2\nvAhYD7y9Pf+bnj/vDXTv7/ts4HHg4NZ+EHBHjzluA/4XcDLwY+3j1rb8Yz3m+JuR5VuB+W35OcCG\nnr8394x+fcatu73PrwndRbWfBC4BtgGfB84EvrvHHHe0j/sDDwP7tefp82e1nXPDyPmfDfxFW17Y\n5+8w3QWN1cC9wGPAo3Q90auB5/X5NZkk57U9nutg4HeBjwM/P27dRT3meCFwMfAHwPOBd7efm7XA\nET1//Q8b93g+sAk4FDisxxzLRpYPaX9P7gA+CRzeY47VwLy2vBR4ANgIfK3P/zft/LcB7wJe3Od5\nd5BjKfDF9n/4KOB64B/b/8GX9ZjjucB7gLva+bcBNwFv6vnrcR3wTuCFI20vbG1f6DnLD+7kcQKw\ntcccV7XfndOAde35gW3dbT3m+Dxd58/K9vfjne1n9lzg6p6/N08BD457/Gv7+ECPOW4bWf4j4H8C\n3wP8CvDZPjLMtWHDTwEvovunMOqItq43Se7Y2SqgnysTnWdVGypcVZtaT82nk3xPy9Kn7VX1JPCt\nJF+tNuSxqr6dpM/vz1Lg7cBvAb9eVbcn+XZVfanHDADPSnIoXbG2X1VtA6iqf06yvecsdyb5har6\nY+D/JllaVeuTHEv3x7EvVVVPAV8AvpDkAODVwOnA+4G+emKf1YYOP4euYDyErmA7EDigpwyj9qcb\nLnwg3Qsuqurv2tenL2uBPwdOrqqvQ3dVmu7Cwlq6Cw69SPKDO1sFLOkrB10P/f10L6zenORn6IrY\nJ4CTesxxGXAN3c/rF4FPAK+he+H3EbpbafryD0z8H7yArnAq4JiecvwO3QtPgAvoLlD+V7rh939I\n97XpwylVNTaS5veAN1R328yxdIX00p5yQHcB4XnAF5N8HbgC+FRV/X2PGQAuAs5vWf4P8CtV9RNJ\nXtnW/XBPOT4BfAZ4FfB6ut+fK4F3JTm2qn6zpxyLquq9ow3tb+x7k7y5pwxjbgW+xI5fH/bZC/zi\nqvqZtvzZNjrvz5O8tscM0F3o+jBAkrNHvk8fTvKWnrP8OvATdK9bN7RMD1bV0T3nGLW0qsb+534w\nyZl9nHSuFa/vAG5Mcj8wds/cQuAlwNt6znI43R/Eb4xrD90f6748nGRJVd0OUFX/lOSn6IaJ/oce\ncwD8S5JnV9W36K7iAd2wA3q8uNCKow8m+ZP28WFm53fhEOArdD8TleSIqtqa5Ln0f2HhF4EPJXkX\n3YvPv07yEN3v0S/2mONpn3d195auA9YleXaPOS6h62Hcj+4ix58keYCuILmyxxzQXdm8NcnNwMvp\nhnPThlQ/1mMOX2BN5AusiXyB9XT7J9m/qrYDB1XVrQBV9bdJDuwxB8A3qurXgF9L8nK6i4K3JbkH\nuKKq1vSU44CquhYgyXur6tMAVXVjkvf3lAG6v2mXteUPJLm1qn673dp1N9BX8fq1JL8BfKyqHgZo\nwy/fxHdey/blHuCtVXX/+BXtNUFfDkzyrPZ6japalWQL8GXaBdyejN5eefm4dfv1mIOquiDJp+j+\nhj1EdwGo+szQvCDJ/6D7/3tIklTrhqWn21HnVPFaVZ9vVzNP5OkTNt3aevz69L/phuvePn5Fkr/o\nMccZwNN68do/0TOS/GGPOQBe0XokxgrIMQfQ9d70qqo2Az+X5BS6Ycx9n3/RTlY9BbyuxyhUNyHT\nm5IcDBxN97dh89g/0h69YWcr2kWPXlTVB9s/Carq75NcDvw48NGqumXXe894lg8luQH498AFVXVv\na98GvKLHKL7AmsgXWOP4AmuCi4DPJVkNfD7Jh4A/Bf4LMOH1QV+q6i+Bv0xyLt3FhjcAfRWv/y/J\nT9JdwK0kp1XVZ5P8GN0Ik778c5Ifraq/ahecHoPu9UmSPi8gv4FuWOqX2t/UortdZR1dj3Cf3s3O\nfz/O7THHn9H9jtww1lBVl7URAx/uMcfVSZ5bVf9UVe8aa0zyEuC+HnMAT3vd+lq64fZ9XtQf81G6\n+VmgG/UzD9jWRmL18jct3/lbLknSjrUh7ivphqCOTcAz9gJrdVWNH2WyJ7P8LN194RNePIy9EO4p\nx/vo7km7YVz7MuDDVbW4pxzvAd5XI7PJt/aX0H1vfraPHDvI9Vq63qtFVfXCns99/rimi6pq7AXW\n+6rH2eXb7Tr/HTiW7sLgQ8BngUvbxeS+clxZVcv7Ot8ucvwA8D66C7W/Qve1OZOus+GsqupldFqS\n76cb2bKY7r7XN7ce8fnA6VV1YR85WpbvA44EbqrZf1eIQbxDxS5yvHqs536Wc8zq94buQs+Lq+rO\nAX1veslh8SpJmpaR+6Vn3VCymOPfzn8Q33mB5ddkgDlgOFn2xRzpZpQ/h25EyRK6STWvbutuq6qd\n3eM/Z7O0EQFvM8fTsvi9Gctg8SpJmo4kf1dVC2c7BwwnizkmGkoWc0w0lCz7Yo4kG4AfbnOSLAI+\nDXy83TLyN1X1sj5yDCmLOYabZQg55tQ9r5KkPSPDmUF9MFnMMdws5hhuFnNMMKR3hRhKFnMMN8us\n57B4lSTtjqHMoD6kLOYYbhZzDDeLOZ5uSO8KMZQs5hhullnPYfEqSdodQ5lBfUhZzDHcLOYYbhZz\nPN2Q3hViKFnMMdwss57De14lSZIkSYPX53udSZIkSZI0JRavkiRJkqTBs3iVJEmSJA2exaskSZIk\nafAsXiVJkiRJg/f/AQ5egYOmUislAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292814a1780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##get daylist\n",
    "importlib.reload(prepare)\n",
    "df = prepare.month_data(business)\n",
    "prepare.plot_monthdata(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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p52Y3qLPluhqg3m5ZSrnpIXV2eZLPl1K+dKB6e04p5ZQt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\nkucl+e6B4lcnuWaGuWsu29C5n5vke7bMvWrfoeNjlk29zO8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2928225f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prepare.plot_daydata(business)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29280f53a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##generate data\n",
    "data_list =  [1,3,4,5,3,1,3,6,7,5,4,2,3,5,7,8,9]\n",
    "gen = pd.DataFrame({'rentNumber':data_list})\n",
    "prepare.plot_monthdata(gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "business_copy = business.copy()\n",
    "business_copy['rentNumber'] = np.log(business_copy.rentNumber)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列经过0阶差分后归于平稳，p值为0.00469951067314\n"
     ]
    }
   ],
   "source": [
    "##stationarity test,测试样本7\n",
    "prepare.stationarity_test(business_copy,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列为非白噪声序列，对应的p值为：7.81474158571e-60\n",
      "一阶差分序列为非白噪声序列，对应的p值为：0.000493556298573\n"
     ]
    }
   ],
   "source": [
    "##whitenoise_test\n",
    "prepare.whitenoise_test(business_copy,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:402: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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RPQIjAAZnlEO6hj0MzjAVJoG5uYBhmZTh6JPyOFYzPY1OI4/vO5gHntifpw/N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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292edc69ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##acf、pacf\n",
    "importlib.reload(prepare)\n",
    "prepare.plot_acfandpacf(business_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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018l1hRC6I0mTMEp7wpOwszSjfTPHkk+4HApJF+GBb6u+UmNuDeMWw8IQpaB6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29280ef78d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##predict\n",
    "importlib.reload(prepare)\n",
    "import numpy as np\n",
    "pred, RMSE=prepare.arma_predict(business_copy,21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7,  9,  6, 14, 16, 17, 51, 28, 46, 34, 29, 37, 37, 21, 11,  6, 13,\n",
       "        2, 13, 18, 15,  2])"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(pred).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.53236883500699539"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

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提交于 2017-10-24 19:14 . ARMA prediction

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https://gitee.com/orayang_admin/ARMA_prediction.git

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OraYang/ARMA_prediction

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