Python-DA-and-DM/应用系统负载分析与磁盘容量数据/应用系统负载分析与磁盘容量预测.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0 数据探索\n",
    "\n",
    "首先，我们对数据集进行快速浏览和查看，以对我们数据分析的对象有大致的“感观”。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 加载函数库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "# 显示配置\n",
    "%matplotlib inline\n",
    "plt.rcParams['font.family']=['SimHei'] #用来正常显示中文标签 \n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('应用系统负载分析与磁盘容量预测.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "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></th>\n",
       "      <th>SYS_NAME</th>\n",
       "      <th>NAME</th>\n",
       "      <th>TARGET_ID</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>ENTITY</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>COLLECTTIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>财务管理系统</td>\n",
       "      <td>CWXT_DB</td>\n",
       "      <td>184</td>\n",
       "      <td>磁盘已使用大小</td>\n",
       "      <td>C:\\</td>\n",
       "      <td>3.427079e+07</td>\n",
       "      <td>2014-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>财务管理系统</td>\n",
       "      <td>CWXT_DB</td>\n",
       "      <td>184</td>\n",
       "      <td>磁盘已使用大小</td>\n",
       "      <td>D:\\</td>\n",
       "      <td>8.026259e+07</td>\n",
       "      <td>2014-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>财务管理系统</td>\n",
       "      <td>CWXT_DB</td>\n",
       "      <td>183</td>\n",
       "      <td>磁盘容量</td>\n",
       "      <td>C:\\</td>\n",
       "      <td>5.232332e+07</td>\n",
       "      <td>2014-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>财务管理系统</td>\n",
       "      <td>CWXT_DB</td>\n",
       "      <td>183</td>\n",
       "      <td>磁盘容量</td>\n",
       "      <td>D:\\</td>\n",
       "      <td>1.572833e+08</td>\n",
       "      <td>2014-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>财务管理系统</td>\n",
       "      <td>CWXT_DB</td>\n",
       "      <td>184</td>\n",
       "      <td>磁盘已使用大小</td>\n",
       "      <td>C:\\</td>\n",
       "      <td>3.432890e+07</td>\n",
       "      <td>2014-10-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SYS_NAME     NAME  TARGET_ID DESCRIPTION ENTITY         VALUE COLLECTTIME\n",
       "0   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.427079e+07  2014-10-01\n",
       "1   财务管理系统  CWXT_DB        184     磁盘已使用大小    D:\\  8.026259e+07  2014-10-01\n",
       "2   财务管理系统  CWXT_DB        183        磁盘容量    C:\\  5.232332e+07  2014-10-01\n",
       "3   财务管理系统  CWXT_DB        183        磁盘容量    D:\\  1.572833e+08  2014-10-01\n",
       "4   财务管理系统  CWXT_DB        184     磁盘已使用大小    C:\\  3.432890e+07  2014-10-02"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 前五行数据预览\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 188 entries, 0 to 187\n",
      "Data columns (total 7 columns):\n",
      "SYS_NAME       188 non-null object\n",
      "NAME           188 non-null object\n",
      "TARGET_ID      188 non-null int64\n",
      "DESCRIPTION    188 non-null object\n",
      "ENTITY         188 non-null object\n",
      "VALUE          188 non-null float64\n",
      "COLLECTTIME    188 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(1), int64(1), object(4)\n",
      "memory usage: 7.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# 基本属性信息\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "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></th>\n",
       "      <th>TARGET_ID</th>\n",
       "      <th>VALUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>188.000000</td>\n",
       "      <td>1.880000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>8.230415e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.501335</td>\n",
       "      <td>4.698926e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>3.321187e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>4.816875e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>6.629296e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>184.000000</td>\n",
       "      <td>1.066458e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>184.000000</td>\n",
       "      <td>1.572833e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        TARGET_ID         VALUE\n",
       "count  188.000000  1.880000e+02\n",
       "mean   183.500000  8.230415e+07\n",
       "std      0.501335  4.698926e+07\n",
       "min    183.000000  3.321187e+07\n",
       "25%    183.000000  4.816875e+07\n",
       "50%    183.500000  6.629296e+07\n",
       "75%    184.000000  1.066458e+08\n",
       "max    184.000000  1.572833e+08"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数值属性的中心趋势度量1\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
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1gZWq54D/jNx6bPOfYe3KwIpBB9CzxwoeaccWenXFMaGv+NyePPDZi8JWrNu2JjvvImTj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\nDDz/D63YVBnQtNJhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#　数值属性的简单图示\n",
    "plt.figure(figsize=(12,6))\n",
    "data['VALUE'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "目前看起来只是没有特别异常的数据点。我们继续对具体C/D盘的存储数据进行探索。\n",
    "\n",
    "**首先看看C/D盘的磁盘容量数据**，我们期待看到这个数据值的变化应该不大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "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></th>\n",
       "      <th>VALUE_C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLLECTTIME</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-10-01</th>\n",
       "      <td>52323324.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-02</th>\n",
       "      <td>52323324.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-03</th>\n",
       "      <td>52323324.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-04</th>\n",
       "      <td>52323324.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-05</th>\n",
       "      <td>52323324.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                VALUE_C\n",
       "COLLECTTIME            \n",
       "2014-10-01   52323324.0\n",
       "2014-10-02   52323324.0\n",
       "2014-10-03   52323324.0\n",
       "2014-10-04   52323324.0\n",
       "2014-10-05   52323324.0"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取C盘磁盘容量 (注意转义字符的使用)\n",
    "disk_storage = data.loc[(data['ENTITY'] == 'C:\\\\') & (data['TARGET_ID'] == 183),['COLLECTTIME','VALUE']]\n",
    "disk_storage.set_index('COLLECTTIME',inplace = True)\n",
    "disk_storage.columns = ['VALUE_C']\n",
    "disk_storage.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "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></th>\n",
       "      <th>VALUE_C</th>\n",
       "      <th>VALUE_D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLLECTTIME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-10-01</th>\n",
       "      <td>52323324.0</td>\n",
       "      <td>157283328.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-02</th>\n",
       "      <td>52323324.0</td>\n",
       "      <td>157283328.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-03</th>\n",
       "      <td>52323324.0</td>\n",
       "      <td>157283328.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-04</th>\n",
       "      <td>52323324.0</td>\n",
       "      <td>157283328.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-05</th>\n",
       "      <td>52323324.0</td>\n",
       "      <td>157283328.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                VALUE_C      VALUE_D\n",
       "COLLECTTIME                         \n",
       "2014-10-01   52323324.0  157283328.0\n",
       "2014-10-02   52323324.0  157283328.0\n",
       "2014-10-03   52323324.0  157283328.0\n",
       "2014-10-04   52323324.0  157283328.0\n",
       "2014-10-05   52323324.0  157283328.0"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取D盘磁盘容量 (注意转义字符的使用)\n",
    "disk_storage['VALUE_D'] = data.loc[(data['ENTITY'] == 'D:\\\\') & (data['TARGET_ID'] == 183), ['VALUE']].values\n",
    "disk_storage.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bmSXGwW5mlhgHu5lZYhzsZmaJcbCbmSXGwW5mlhgHu5lZYhzsZmaJcbCbmSXGwW5mlpiq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\nlhgHu5lZYhzsZmaJ+f8wBQ6Uwx+ZmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 简单的图示\n",
    "plt.figure(figsize=(12,6))\n",
    "disk_storage[[\"VALUE_C\", \"VALUE_D\"]].plot()\n",
    "plt.ylim([0.3e+8, 2e+8])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "其次，我们来观察一下C/D磁盘的已可用存储使用变化情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "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></th>\n",
       "      <th>VALUE_C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLLECTTIME</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-10-01</th>\n",
       "      <td>34270787.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-02</th>\n",
       "      <td>34328899.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-03</th>\n",
       "      <td>34327553.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-04</th>\n",
       "      <td>34288672.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-05</th>\n",
       "      <td>34190978.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 VALUE_C\n",
       "COLLECTTIME             \n",
       "2014-10-01   34270787.33\n",
       "2014-10-02   34328899.02\n",
       "2014-10-03   34327553.50\n",
       "2014-10-04   34288672.21\n",
       "2014-10-05   34190978.41"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取C盘已使用情况\n",
    "disk_usage = data.loc[(data['ENTITY'] == 'C:\\\\') & (data['TARGET_ID'] == 184),['COLLECTTIME','VALUE']]\n",
    "disk_usage.set_index('COLLECTTIME',inplace = True)\n",
    "disk_usage.columns = ['VALUE_C']\n",
    "disk_usage.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "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></th>\n",
       "      <th>VALUE_C</th>\n",
       "      <th>VALUE_D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLLECTTIME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-10-01</th>\n",
       "      <td>34270787.33</td>\n",
       "      <td>80262592.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-02</th>\n",
       "      <td>34328899.02</td>\n",
       "      <td>83200151.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-03</th>\n",
       "      <td>34327553.50</td>\n",
       "      <td>83208320.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-04</th>\n",
       "      <td>34288672.21</td>\n",
       "      <td>83099271.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-05</th>\n",
       "      <td>34190978.41</td>\n",
       "      <td>82765171.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 VALUE_C      VALUE_D\n",
       "COLLECTTIME                          \n",
       "2014-10-01   34270787.33  80262592.65\n",
       "2014-10-02   34328899.02  83200151.65\n",
       "2014-10-03   34327553.50  83208320.00\n",
       "2014-10-04   34288672.21  83099271.65\n",
       "2014-10-05   34190978.41  82765171.65"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取D盘已使用情况\n",
    "disk_usage['VALUE_D'] = data.loc[(data['ENTITY'] == 'D:\\\\') & (data['TARGET_ID'] == 184),['VALUE']].values\n",
    "disk_usage.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ARaRzvcPN1FLp6tpaKimxzZx+Olx1FTz5sGqbJfcUlEW6j2Jd0Gesi+kgY4wB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\nkiRJUg82ypIkSVIP/w8D6LLy9U461QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 简单的图示\n",
    "plt.figure(figsize=(12,6))\n",
    "disk_usage[\"VALUE_C\"].plot(color='blue',linewidth=2.0,linestyle='-',marker='o',markersize=12,markerfacecolor='r')\n",
    "plt.title(\"C盘已使用情况\")\n",
    "plt.xlabel('日期')\n",
    "plt.xticks(disk_usage.index, rotation = 30)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IB4a88w4kEnDYYVBR4fVoxK8UikUkcIxxZn7iRBh0RFcsIb7y9a787sGISibE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\nSZKk3vt/vWQH/Xe0TJoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 简单的图示\n",
    "plt.figure(figsize=(12,6))\n",
    "disk_usage[\"VALUE_D\"].plot(color='blue',linewidth=2.0,linestyle='-',marker='o',markersize=12,markerfacecolor='r')\n",
    "plt.title(\"D盘已使用情况\")\n",
    "plt.xlabel('日期')\n",
    "plt.xticks(disk_usage.index, rotation = 30)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "再次检查一下到目前为止经过整理的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 47 entries, 2014-10-01 to 2014-11-16\n",
      "Data columns (total 2 columns):\n",
      "VALUE_C    47 non-null float64\n",
      "VALUE_D    47 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 1.1 KB\n"
     ]
    }
   ],
   "source": [
    "disk_usage.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "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></th>\n",
       "      <th>VALUE_C</th>\n",
       "      <th>VALUE_D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.700000e+01</td>\n",
       "      <td>4.700000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.483490e+07</td>\n",
       "      <td>8.477504e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.716905e+05</td>\n",
       "      <td>2.115162e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.321187e+07</td>\n",
       "      <td>8.026259e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.432781e+07</td>\n",
       "      <td>8.320424e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.482887e+07</td>\n",
       "      <td>8.450061e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.547896e+07</td>\n",
       "      <td>8.630771e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.570501e+07</td>\n",
       "      <td>8.976660e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            VALUE_C       VALUE_D\n",
       "count  4.700000e+01  4.700000e+01\n",
       "mean   3.483490e+07  8.477504e+07\n",
       "std    6.716905e+05  2.115162e+06\n",
       "min    3.321187e+07  8.026259e+07\n",
       "25%    3.432781e+07  8.320424e+07\n",
       "50%    3.482887e+07  8.450061e+07\n",
       "75%    3.547896e+07  8.630771e+07\n",
       "max    3.570501e+07  8.976660e+07"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disk_usage.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "### 1. 数据预处理\n",
    "\n",
    "对于时间序列数据，我们首先计算该时间序列的相关性；主要是自相关和偏自相关性。\n",
    "\n",
    "**自相关（autocorrelation 或者 ACF）**，也叫序列相关，是一个信号于其自身在不同时间点的相关度。非正式地来说，它就是两次观察之间的相似度对它们之间的时间差的函数。它是找出重复模式（如被噪声掩盖的周期信号），或识别隐含在信号谐波频率中消失的基频的数学工具。它常用于信号处理中，用来分析函数或一系列值，如时域信号。\n",
    "\n",
    "**偏自相关（partial autocorrelation）**的是在排除了其他变量干扰的情况下，单独研究特定某两个变量之间的关系；比如，偏相关系数为负值说明在排除了其他变量影响的情况下，对于两个变量，一个变量的增加引起另一个变量的减少。在自相关性计算过程中，算出滞后k的自相关系数 时，实际上得到并不是Z(t)与Z(t-k)之间单纯的相关关系。因为Z(t)同时还会受到中间k-1个随机变量Z(t-1)、Z(t-2)、……、Z(t-k+1)的影响，而这k-1个随机变量又都和z(t-k)具有相关关系，所以**自相关系数**里面实际掺杂了其他变量对Z(t)与Z(t-k)的影响。\n",
    "\n",
    "为了能单纯测度Z(t-k)对Z(t)的影响，引进**偏自相关系数（PACF）**的概念。对于平稳时间序列{Z(t)}，所谓滞后k偏自相关系数指在给定中间k-1个随机变量Z(t-1)、Z(t-2)、……、Z(t-k+1)的条件下，或者说，在剔除了中间k-1个随机变量Z(t-1)、Z(t-2)、……、Z(t-k+1)的干扰之后，Z(t-k)对Z(t)影响的相关程度。\n",
    "\n",
    "参考资料：\n",
    "1. <a href='https://machinelearningmastery.com/gentle-introduction-autocorrelation-partial-autocorrelation/' target='_blank'>A Gentle Introduction to Autocorrelation and Partial Autocorrelation</a>\n",
    "2. <a href='https://zhuanlan.zhihu.com/p/54153963' target='_blank'>时间序列（一）：平稳性、自相关函数与LB检验</a>\n",
    "3. <a href='https://blog.csdn.net/Yuting_Sunshine/article/details/95317735' target='_blank'>如何理解自相关和偏自相关图</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bt9XKyoEvA96Tmd9vqdcaZ6RaY8u2PUdMh1vsenqCN195Hq/9j79NdcWp/Mbr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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x1152 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 自相关与偏相关 - C盘\n",
    "import statsmodels.api as sm\n",
    "\n",
    "C_usage = disk_usage['VALUE_C']\n",
    "fig = plt.figure(figsize = (12,16))\n",
    "ax1 = fig.add_subplot(411)\n",
    "sm.graphics.tsa.plot_acf(C_usage, lags = 40, ax = ax1)\n",
    "ax1.set_title('自相关图 - C盘已使用存储空间的时间序列')\n",
    "\n",
    "ax2 = fig.add_subplot(412)\n",
    "sm.graphics.tsa.plot_pacf(C_usage, lags = 40, ax = ax2)\n",
    "ax2.set_title('偏自相关图 - C盘已使用存储空间的时间序列')\n",
    "\n",
    "#一阶差分后去空值取自相关系数\n",
    "C_usage_diff = C_usage.diff(1).dropna() \n",
    "ax3 = fig.add_subplot(413)\n",
    "sm.graphics.tsa.plot_acf(C_usage_diff, lags = 40, ax = ax3)\n",
    "ax3.set_title('自相关图 - 一阶差分')\n",
    "\n",
    "ax4 = fig.add_subplot(414)\n",
    "sm.graphics.tsa.plot_pacf(C_usage_diff, lags = 40, ax = ax4)\n",
    "ax4.set_title('偏自相关图 - 一阶差分')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S/IiI+6fTzimCkiRJklQRA5YkSZIkVcSAJUmSJEkVMWBJkiRJUkUMWJIkSZJUEQOWJEmS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\n/w+QT0bN2eQXAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x1152 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 自相关与偏相关 - D盘\n",
    "D_usage = disk_usage['VALUE_D']\n",
    "fig = plt.figure(figsize = (12,16))\n",
    "ax1 = fig.add_subplot(411)\n",
    "sm.graphics.tsa.plot_acf(D_usage, lags = 40, ax = ax1)\n",
    "ax1.set_title('自相关图 - D盘已使用存储空间的时间序列')\n",
    "\n",
    "ax2 = fig.add_subplot(412)\n",
    "sm.graphics.tsa.plot_pacf(D_usage, lags = 40, ax = ax2)\n",
    "ax2.set_title('偏自相关图 - D盘已使用存储空间的时间序列')\n",
    "\n",
    "#一阶差分后去空值取自相关系数\n",
    "D_usage_diff = D_usage.diff(1).dropna() \n",
    "ax3 = fig.add_subplot(413)\n",
    "sm.graphics.tsa.plot_acf(D_usage_diff, lags = 40, ax = ax3)\n",
    "ax3.set_title('自相关图 - 一阶差分')\n",
    "\n",
    "ax4 = fig.add_subplot(414)\n",
    "sm.graphics.tsa.plot_pacf(D_usage_diff, lags = 40, ax = ax4)\n",
    "ax4.set_title('偏自相关图 - 一阶差分')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "我们需要进一步对**时间序列的平稳性进行测试**. \n",
    "\n",
    "1. **什么是平稳性呢？**直观上看当数据没有明显的模式特征的话（趋势性、季节性），我们认为它是平稳的。定义上“平稳”指固定时间和位置的概率分布与所有时间和位置的概率分布相同的随机过程。其数学期望和方差这些参数也不随时间和位置变化。在统计模型中，我们一般用ADF检验来测试时间序列的平稳性。\n",
    "\n",
    "2. **为什么要做平稳性测试呢？**平稳是自回归模型ARMA的必要条件，因此对于时间序列，首先要保证应用自回归的差分序列是平稳的。\n",
    "\n",
    "3. **如何对时间序列做平稳性测试呢？** 迪基-福勒检验（Dickey-Fuller test）和扩展迪基-福勒检验（Augmented Dickey-Fuller test 或ADF）可以测试一个自回归模型ARMA是否存在单位根（unit root）。数学上可以证明，时间序列中存在单位根过程就不平稳，会使回归分析中存在伪回归。\n",
    "\n",
    "参考资料：\n",
    "1. <a href='https://pengfoo.com/post/machine-learning/2017-01-24' target='_blank'>Python时间序列平稳检验--ADF检验</a>\n",
    "2. <a href='https://baike.baidu.com/item/%E5%B9%B3%E7%A8%B3%E9%9A%8F%E6%9C%BA%E8%BF%87%E7%A8%8B' target='_blank'>平稳随机过程</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C盘已使用存储空间的时间序列经过1阶差分后归于平稳，p值为9.572975592333248e-07\n"
     ]
    }
   ],
   "source": [
    "# ADF检验  \n",
    "# 注意：预留最后5个数字用于对模型性能进行评估\n",
    "data = disk_usage.iloc[:len(disk_usage)-5]\n",
    "\n",
    "#平稳性测试函数\n",
    "from statsmodels.tsa.stattools import adfuller as ADF\n",
    "diff = 0 \n",
    "adf = ADF(data['VALUE_C'])\n",
    "\n",
    "#adf[1]为p值，p值小于0.05认为是平稳的\n",
    "while adf[1] >= 0.05:\n",
    "    diff = diff + 1\n",
    "    adf = ADF(data['VALUE_C'].diff(diff).dropna())\n",
    "    \n",
    "print('C盘已使用存储空间的时间序列经过%s阶差分后归于平稳，p值为%s' % (diff,adf[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D盘已使用存储空间的时间序列经过1阶差分后归于平稳，p值为4.79259126339371e-07\n"
     ]
    }
   ],
   "source": [
    "diff = 0 \n",
    "adf = ADF(data['VALUE_D'])\n",
    "\n",
    "#adf[1]为p值，p值小于0.05认为是平稳的\n",
    "while adf[1] >= 0.05:\n",
    "    diff = diff + 1\n",
    "    adf = ADF(data['VALUE_D'].diff(diff).dropna())\n",
    "    \n",
    "print('D盘已使用存储空间的时间序列经过%s阶差分后归于平稳，p值为%s' % (diff,adf[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "经过ADF检验，我们可以认为C/D盘已使用存储空间的时间序列都是稳定的时间序列。下面进行白噪声测试\n",
    "\n",
    "1. **什么是白噪声?** 随机变量X（t）（t=1，2，3……），如果是由一个不相关的随机变量的序列构成的，即对于所有s≠k，随机变量X(s)和X(k)的协方差为零，则称其为纯随机过程。如果一个纯随机过程的期望和方差均为常数，则称之为白噪声过程。白噪声过程的样本实称成为白噪声序列，简称白噪声。\n",
    "2. **为什么要做白噪声测试?** 因为白噪声无法预测，所有自相关接近零。如果某个时间序列是白噪声，那么任何回归分析都是没有意义的！\n",
    "3. **如何对时间序列做白噪声测试?** 原理上还是依赖于序列的自相关性；比如acorr_ljungbox方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C盘已使用存储空间的时间序列为 非白噪声序列，对应的p值为：1.0609907508070775e-08\n",
      "其一阶差分为 白噪声序列，对应的p值为：0.4745522552554281\n"
     ]
    }
   ],
   "source": [
    "# 白噪声检验\n",
    "# LB统计量\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "\n",
    "[[lb],[p]] = acorr_ljungbox(data['VALUE_C'], lags=1)\n",
    "if p < 0.05:\n",
    "    print('C盘已使用存储空间的时间序列为 非白噪声序列，对应的p值为：%s'%p)\n",
    "else:\n",
    "    print('C盘已使用存储空间的时间序列为 白噪声序列，对应的p值为：%s'%p)\n",
    "    \n",
    "[[lb],[p]] = acorr_ljungbox(data['VALUE_C'].diff(1).dropna(),lags=1)\n",
    "if p < 0.05:\n",
    "    print('其一阶差分序列为 非白噪声序列，对应的p值为：%s'%p)\n",
    "else:\n",
    "    print('其一阶差分为 白噪声序列，对应的p值为：%s'%p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D盘已使用存储空间的时间序列为 非白噪声序列，对应的p值为：9.95850372977218e-06\n",
      "其一阶差分为 白噪声序列，对应的p值为：0.1143302597764247\n"
     ]
    }
   ],
   "source": [
    "[[lb],[p]] = acorr_ljungbox(data['VALUE_D'], lags=1)\n",
    "if p < 0.05:\n",
    "    print('D盘已使用存储空间的时间序列为 非白噪声序列，对应的p值为：%s'%p)\n",
    "else:\n",
    "    print('D盘已使用存储空间的时间序列为 白噪声序列，对应的p值为：%s'%p)\n",
    "    \n",
    "[[lb],[p]] = acorr_ljungbox(data['VALUE_D'].diff(1).dropna(),lags=1)\n",
    "if p < 0.05:\n",
    "    print('其一阶差分序列为 非白噪声序列，对应的p值为：%s'%p)\n",
    "else:\n",
    "    print('其一阶差分为 白噪声序列，对应的p值为：%s'%p)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 时间序列建模\n",
    "\n",
    "传统的时间序列建模采用ARIMA模型，即Auto Regressive Integrated Moving Average模型。这是一个包括多个子模型的家族，如下\n",
    "1. 自回归模型（AR）：用变量自身的历史时间数据对变量进行回归，从而预测变量未来的时间数据。\n",
    "2. 移动平均模型（MA）：移动平均模型关注的是误差项的累加，能够有效消除预测中的随机波动。\n",
    "3. 自回归移动平均模型（ARMA）：多阶数的AR和MA模型\n",
    "4. 自回归差分移动平均模型（ARIMA）：带差分计算的ARMA\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "我们将用ARIMA模型在本例中基于时间序列进行预测；基本步骤如下\n",
    "1. 对序列绘图，进行 ADF 检验，观察序列是否平稳；\n",
    "2. 对于非平稳时间序列要先进行 d 阶差分，转化为平稳时间序列\n",
    "3. 对平稳时间序列分别求得其自相关系数（ACF）和偏自相关系数（PACF），通过对自相关图和偏自相关图的分析，得到最佳的阶数p/q；\n",
    "4. 基于模型参数d/q/p ，得到ARIMA 模型，最后进行模型检验。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 获得模型参数 - 最佳p/q值\n",
    "def arima_para(time_series, d=1):\n",
    "    \"\"\" 对于给定的平稳时间序列，基于BIC指标的ARIMA模型最佳p/q值\n",
    "    \n",
    "    d - 差分阶数；默认为1阶\n",
    "    \"\"\"\n",
    "    \n",
    "    #定阶 \n",
    "    pmax = int(len(time_series)/10)   #一般阶数不超过length/10\n",
    "    qmax = int(len(time_series)/10)\n",
    "    \n",
    "    bic_matrix = []         #bic矩阵\n",
    "    for p in range(pmax+1):\n",
    "        tmp = []\n",
    "        for q in range(qmax+1):\n",
    "            try:\n",
    "                tmp.append(ARIMA(time_series,(p,d,q)).fit().bic)\n",
    "            except:\n",
    "                tmp.append(None)\n",
    "            \n",
    "        bic_matrix.append(tmp)\n",
    "    \n",
    "    bic_matrix = pd.DataFrame(bic_matrix) \n",
    "    p,q = bic_matrix.stack().astype('float64').idxmin()\n",
    "    \n",
    "    return p, q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C盘已使用存储空间的时间序列： ARIMA模型最佳p值和q值为:0、0\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "x_C = data['VALUE_C']\n",
    "p_C,q_C  = arima_para(x_C)\n",
    "print('C盘已使用存储空间的时间序列： ARIMA模型最佳p值和q值为:%s、%s'%(p_C,q_C))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D盘已使用存储空间的时间序列： ARIMA模型最佳p值和q值为:0、1\n"
     ]
    }
   ],
   "source": [
    "x_D = data['VALUE_D']\n",
    "p_D,q_D  = arima_para(x_D)\n",
    "print('D盘已使用存储空间的时间序列： ARIMA模型最佳p值和q值为:%s、%s'%(p_D,q_D))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对ARIMI模型进行检验；即判断从时间序列中提取有效信息后剩下的序列是否是白噪声。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C盘已使用存储空间的时间序列: 模型ARIMA（[0.47455226 0.45421413 0.10528567 0.05642716 0.09648414 0.15565755\n",
      " 0.05479088 0.08700279 0.12206878 0.16186804 0.20217712 0.13953557],1,1）符合白噪声检验\n"
     ]
    }
   ],
   "source": [
    "# 计算模型预测输出\n",
    "arima_C = ARIMA(x_C,(p_C,1,q_C)).fit()\n",
    "x_pred = arima_C.predict(typ='levels')\n",
    "\n",
    "# 获得原始时间序列的残差\n",
    "x_err = (x_pred - x_C).dropna()\n",
    "\n",
    "# 检查残差序列是否是白噪声\n",
    "lagnum = 12\n",
    "lb, p = acorr_ljungbox(x_err, lags = lagnum)\n",
    "\n",
    "# p值小于0.05，认为是非白噪声\n",
    "h = (p < 0.05).sum()\n",
    "if h > 0:\n",
    "    print('C盘已使用存储空间的时间序列: 模型ARIMA（%s,1,%s）不符合白噪声检验'%(p,q))\n",
    "else:\n",
    "    print('C盘已使用存储空间的时间序列: 模型ARIMA（%s,1,%s）符合白噪声检验'%(p,q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D盘已使用存储空间的时间序列: 模型ARIMA（[0.12693165 0.28086103 0.10578867 0.15967815 0.21700411 0.31311161\n",
      " 0.29593954 0.2281762  0.18261458 0.12747894],1,1）符合白噪声检验\n"
     ]
    }
   ],
   "source": [
    "# 计算模型预测输出\n",
    "arima_D = ARIMA(x_D,(p_D,1,q_D)).fit()\n",
    "x_pred = arima_D.predict(typ='levels')\n",
    "\n",
    "# 获得原始时间序列的残差\n",
    "x_err = (x_pred - x_D).dropna()\n",
    "\n",
    "# 检查残差序列是否是白噪声\n",
    "lagnum = 10\n",
    "lb, p = acorr_ljungbox(x_err, lags = lagnum)\n",
    "\n",
    "#p值小于0.05，认为是非白噪声\n",
    "h = (p < 0.05).sum()\n",
    "if h > 0:\n",
    "    print('D盘已使用存储空间的时间序列: 模型ARIMA（%s,1,%s）不符合白噪声检验'%(p,q))\n",
    "else:\n",
    "    print('D盘已使用存储空间的时间序列: 模型ARIMA（%s,1,%s）符合白噪声检验'%(p,q))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 模型预测\n",
    "\n",
    "基于学得的ARIMA模型预测最后5个数字，并与原始数据进行比较，评估模型性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>C盘使用实际值</th>\n",
       "      <th>C盘使用预测值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLLECTTIME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-12</th>\n",
       "      <td>35704312.58</td>\n",
       "      <td>35722538.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-13</th>\n",
       "      <td>35704980.73</td>\n",
       "      <td>35757103.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-14</th>\n",
       "      <td>34570385.45</td>\n",
       "      <td>35791669.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-15</th>\n",
       "      <td>34673820.69</td>\n",
       "      <td>35826234.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-16</th>\n",
       "      <td>34793245.31</td>\n",
       "      <td>35860800.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 C盘使用实际值      C盘使用预测值\n",
       "COLLECTTIME                          \n",
       "2014-11-12   35704312.58  35722538.09\n",
       "2014-11-13   35704980.73  35757103.59\n",
       "2014-11-14   34570385.45  35791669.08\n",
       "2014-11-15   34673820.69  35826234.58\n",
       "2014-11-16   34793245.31  35860800.07"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# C盘使用情况预测\n",
    "y_forecast = arima_C.forecast(5)[0]\n",
    "y_true = disk_usage.iloc[len(disk_usage)-5:]['VALUE_C']\n",
    "\n",
    "comp_C = pd.DataFrame({\"C盘使用预测值\":y_forecast, \"C盘使用实际值\":y_true})\n",
    "comp_C = comp_C.applymap(lambda x :'%.2f'%x)\n",
    "comp_C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C盘已使用存储空间的时间序列：\n",
      "\n",
      "平均绝对误差为：702320.1312,\n",
      "均方根误差为：3.9350,\n",
      "平均绝对百分误差为：0.020243\n"
     ]
    }
   ],
   "source": [
    "# 性能评估\n",
    "abs_ = (y_forecast - y_true).abs()\n",
    "mae_ = abs_.mean()\n",
    "rmse_ = ((abs_**2).mean())**0.05\n",
    "mape_ = (abs_/y_true).mean()\n",
    "\n",
    "print('C盘已使用存储空间的时间序列：\\n\\n平均绝对误差为：%0.4f,\\n均方根误差为：%0.4f,\\n平均绝对百分误差为：%0.6f' % (mae_, rmse_, mape_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "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></th>\n",
       "      <th>D盘使用实际值</th>\n",
       "      <th>D盘使用预测值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLLECTTIME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-12</th>\n",
       "      <td>87249335.55</td>\n",
       "      <td>88034300.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-13</th>\n",
       "      <td>86986142.20</td>\n",
       "      <td>88217005.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-14</th>\n",
       "      <td>86678240.00</td>\n",
       "      <td>88399711.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-15</th>\n",
       "      <td>89766600.00</td>\n",
       "      <td>88582417.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-16</th>\n",
       "      <td>89377527.25</td>\n",
       "      <td>88765122.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 D盘使用实际值      D盘使用预测值\n",
       "COLLECTTIME                          \n",
       "2014-11-12   87249335.55  88034300.15\n",
       "2014-11-13   86986142.20  88217005.86\n",
       "2014-11-14   86678240.00  88399711.57\n",
       "2014-11-15   89766600.00  88582417.27\n",
       "2014-11-16   89377527.25  88765122.98"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# D盘使用情况预测\n",
    "y_forecast = arima_D.forecast(5)[0]\n",
    "y_true = disk_usage.iloc[len(disk_usage)-5:]['VALUE_D']\n",
    "\n",
    "comp_D = pd.DataFrame({\"D盘使用预测值\":y_forecast, \"D盘使用实际值\":y_true})\n",
    "comp_D = comp_D.applymap(lambda x :'%.2f'%x)\n",
    "comp_D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D盘已使用存储空间的时间序列：\n",
      "\n",
      "平均绝对误差为：1106777.3644,\n",
      "均方根误差为：4.0449,\n",
      "平均绝对百分误差为：0.012610\n"
     ]
    }
   ],
   "source": [
    "# 性能评估\n",
    "abs_ = (y_forecast - y_true).abs()\n",
    "mae_ = abs_.mean()\n",
    "rmse_ = ((abs_**2).mean())**0.05\n",
    "mape_ = (abs_/y_true).mean()\n",
    "\n",
    "print('D盘已使用存储空间的时间序列：\\n\\n平均绝对误差为：%0.4f,\\n均方根误差为：%0.4f,\\n平均绝对百分误差为：%0.6f' % (mae_, rmse_, mape_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 总结\n",
    "\n",
    "看起来我们模型的性能还不错，因为均方误差和百分比误差都比较低；这也是传统的时间序列分析方法的优势，计算量相对较小速度很快。但是，如果仔细对比预测和实际值，模型的表现还是有待提高。各位同学如有兴趣，还可以利用深度学习里面的RNN/LSTM进行建模，可能会获得更好的结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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