zks_011_差分方法变换ARIMA建模预测-世界确诊人数预测.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import numpy as np             #for numerical computations like log,exp,sqrt etc\n",
    "import pandas as pd            #for reading & storing data, pre-processing\n",
    "import matplotlib.pylab as plt #for visualization\n",
    "#for making sure matplotlib plots are generated in Jupyter notebook itself\n",
    "%matplotlib inline             \n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.stattools import acf, pacf\n",
    "\n",
    "##分解(decomposing) 可以用来把时序数据中的趋势和周期性数据都分离出来:\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from statsmodels.tsa.arima_model import ARMA\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 10, 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmedCount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>1312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>4579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>6061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>7816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>9821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>11948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>14551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>17387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>20626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>24553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>28276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>31439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>34876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>37612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-09</td>\n",
       "      <td>40234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>42760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>44789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>60120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>64456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>66900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>69044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>71231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>73260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>75138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>75669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>76202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>77672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-22</td>\n",
       "      <td>78635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>79202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>79774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>80427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>81406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>82794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>84186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>86602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>88377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>90308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>92880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>95181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>98088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>101954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>106016</td>\n",
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       "    <tr>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>109936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>114285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>118905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>126054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>134529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>144031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>156433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>169362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>182406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>198178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>199193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmedCount\n",
       "日期                        \n",
       "2020-01-20             282\n",
       "2020-01-21             314\n",
       "2020-01-22             581\n",
       "2020-01-23             844\n",
       "2020-01-24            1312\n",
       "2020-01-25            2015\n",
       "2020-01-26            2801\n",
       "2020-01-27            4579\n",
       "2020-01-28            6061\n",
       "2020-01-29            7816\n",
       "2020-01-30            9821\n",
       "2020-01-31           11948\n",
       "2020-02-01           14551\n",
       "2020-02-02           17387\n",
       "2020-02-03           20626\n",
       "2020-02-04           24553\n",
       "2020-02-05           28276\n",
       "2020-02-06           31439\n",
       "2020-02-07           34876\n",
       "2020-02-08           37612\n",
       "2020-02-09           40234\n",
       "2020-02-10           42760\n",
       "2020-02-11           44789\n",
       "2020-02-12           60120\n",
       "2020-02-13           64456\n",
       "2020-02-14           66900\n",
       "2020-02-15           69044\n",
       "2020-02-16           71231\n",
       "2020-02-17           73260\n",
       "2020-02-18           75138\n",
       "2020-02-19           75669\n",
       "2020-02-20           76202\n",
       "2020-02-21           77672\n",
       "2020-02-22           78635\n",
       "2020-02-23           79202\n",
       "2020-02-24           79774\n",
       "2020-02-25           80427\n",
       "2020-02-26           81406\n",
       "2020-02-27           82794\n",
       "2020-02-28           84186\n",
       "2020-02-29           86602\n",
       "2020-03-01           88377\n",
       "2020-03-02           90308\n",
       "2020-03-03           92880\n",
       "2020-03-04           95181\n",
       "2020-03-05           98088\n",
       "2020-03-06          101954\n",
       "2020-03-07          106016\n",
       "2020-03-08          109936\n",
       "2020-03-09          114285\n",
       "2020-03-10          118905\n",
       "2020-03-11          126054\n",
       "2020-03-12          134529\n",
       "2020-03-13          144031\n",
       "2020-03-14          156433\n",
       "2020-03-15          169362\n",
       "2020-03-16          182406\n",
       "2020-03-17          198178\n",
       "2020-03-18          199193"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data = pd.read_csv('./world.csv', index_col=0)#1月20日-3月18日 世界总确诊人数\n",
    "#增加id递增列\n",
    "Data = Data.reset_index()\n",
    "##该步骤很重要！！建立时序数据\n",
    "#Parse strings to datetime type  将字符串解析为datetime类型\n",
    "#convert from string to datetime ###Name: date, dtype: datetime64[ns]\n",
    "Data['日期']= pd.to_datetime(Data['日期'],infer_datetime_format=True) \n",
    "#set_index( ) 将 DataFrame 中的列转化为行索引\n",
    "indexedDataset = Data.set_index(['日期'])\n",
    "#数据反转\n",
    "indexedDataset = indexedDataset.iloc[::-1]\n",
    "indexedDataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\converter.py:103: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x193e33b1208>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# plot graph\n",
    "#设置画布大小\n",
    "plt.figure(figsize=(15,5),dpi = 80)\n",
    "#横轴坐标旋转45°\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('confirmedCount')\n",
    "plt.plot(indexedDataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 三、时序数据变换，获得稳定性数据  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1构建稳定性检验函数：  \n",
    "    #滑动平均值  \n",
    "    #滑动标准差  \n",
    "    #Augmented Dickey–Fuller ADF稳定性检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#构建稳定性检验函数\n",
    "def test_stationarity(timeseries):  \n",
    "    \n",
    "    #Determine rolling statistics\n",
    "    movingAverage = timeseries.rolling(window=12).mean()  #滑动平均值\n",
    "    movingSTD = timeseries.rolling(window=12).std()     #滑动标准差\n",
    "    \n",
    "    #Plot rolling statistics\n",
    "    orig = plt.plot(timeseries, color='blue', label='Original')\n",
    "    mean = plt.plot(movingAverage, color='red', label='Rolling Mean')\n",
    "    std = plt.plot(movingSTD, color='black', label='Rolling Std')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.legend(loc='best')\n",
    "    plt.title('Rolling Mean & Standard Deviation')\n",
    "    plt.show(block=False)\n",
    "    \n",
    "    #Perform Dickey–Fuller test:   Augmented Dickey–Fuller ADF稳定性检验\n",
    "    print('Results of Dickey Fuller Test:')\n",
    "    dftest = adfuller(timeseries['confirmedCount'], autolag='AIC')\n",
    "    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "    for key,value in dftest[4].items():\n",
    "        dfoutput['Critical Value (%s)'%key] = value\n",
    "    print(dfoutput)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2  Differencing差分方法-Timeshift transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x193e851ee48>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Estimating trend 估计趋势\n",
    "indexedDataset_logScale = np.log(indexedDataset)  #taking log \n",
    "plt.plot(indexedDataset_logScale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x193e834ef08>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Differencing--差分\n",
    "##这里采用的是一阶差分：一阶差分就是离散函数中连续相邻两项之差。\n",
    "datasetLogDiffShifting_1 = indexedDataset_logScale - indexedDataset_logScale.shift()\n",
    "plt.plot(datasetLogDiffShifting_1)\n",
    "\n",
    "#二阶差分：二阶差分就是一阶差分再进行一次差分。\n",
    "#注：以下内容可以看出，二阶差分后数据已经稳定，所以ARIMA模型参数d=2。\n",
    "datasetLogDiffShifting_2 = datasetLogDiffShifting_1 - datasetLogDiffShifting_1.shift()\n",
    "plt.plot(datasetLogDiffShifting_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x193e83bd6c8>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "example1 = indexedDataset_logScale.diff(1)\n",
    "plt.plot(example1)\n",
    "example2 = example1.diff(1)\n",
    "plt.plot(example2)\n",
    "example3 = example2.diff(1)\n",
    "plt.plot(example3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey Fuller Test:\n",
      "Test Statistic                 -2.847150\n",
      "p-value                         0.051848\n",
      "#Lags Used                      2.000000\n",
      "Number of Observations Used    55.000000\n",
      "Critical Value (1%)            -3.555273\n",
      "Critical Value (5%)            -2.915731\n",
      "Critical Value (10%)           -2.595670\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "datasetLogDiffShifting_1.dropna(inplace=True)#滤除缺失数据。\n",
    "test_stationarity(datasetLogDiffShifting_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey Fuller Test:\n",
      "Test Statistic                -8.336685e+00\n",
      "p-value                        3.258054e-13\n",
      "#Lags Used                     1.000000e+00\n",
      "Number of Observations Used    5.500000e+01\n",
      "Critical Value (1%)           -3.555273e+00\n",
      "Critical Value (5%)           -2.915731e+00\n",
      "Critical Value (10%)          -2.595670e+00\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "datasetLogDiffShifting_2.dropna(inplace=True)#滤除缺失数据。\n",
    "test_stationarity(datasetLogDiffShifting_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "差分后平稳性都要比原数据好很多。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 四、构建模型\n",
    "#### 4.1 自相关图和偏自相关图的分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "\n",
    "#acf   from statsmodels.tsa.stattools import acf, pacf\n",
    "ax1 = fig.add_subplot(211)\n",
    "fig = sm.graphics.tsa.plot_acf(datasetLogDiffShifting_1, lags=20,ax=ax1)\n",
    "ax1.xaxis.set_ticks_position('bottom')\n",
    "fig.tight_layout();\n",
    "\n",
    "#pacf\n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = sm.graphics.tsa.plot_pacf(datasetLogDiffShifting_1, lags=20, ax=ax2)\n",
    "ax2.xaxis.set_ticks_position('bottom')\n",
    "fig.tight_layout();\n",
    "#下图中的阴影表示置信区间，可以看出不同阶数自相关性的变化情况，从而选出p值和q值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "推荐学习视频：\n",
    "【算法+代码案例】时间序列ARIMA模型及预测\n",
    "https://www.bilibili.com/video/BV1dT4y1V7qW\n",
    "\n",
    "p=3/4  q=3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 建立模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:492: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting ARIMA model\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# AR+I+MA = ARIMA model\n",
    "\"\"\"\n",
    "ARIMA模型有三个参数:p,d,q。\n",
    "https://www.cnblogs.com/bradleon/p/6827109.html\n",
    "p--代表预测模型中采用的时序数据本身的滞后数(lags) ,也叫做AR/Auto-Regressive项\n",
    "d--代表时序数据需要进行几阶差分化，才是稳定的，也叫Integrated项。\n",
    "q--代表预测模型中采用的预测误差的滞后数(lags)，也叫做MA/Moving Average项\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "model_3 = ARIMA(indexedDataset_logScale, order=(3,1,3))\n",
    "results_ARIMA = model_3.fit(disp=-1)\n",
    "plt.plot(datasetLogDiffShifting_1)\n",
    "plt.plot(results_ARIMA.fittedvalues, color='red')#模型数据的差分值\n",
    "plt.title('RSS: %.4f'%sum((results_ARIMA.fittedvalues - datasetLogDiffShifting_1['confirmedCount'])**2))\n",
    "print('Plotting ARIMA model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 五、时序数据预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "日期\n",
       "2020-01-21    0.155787\n",
       "2020-01-22    0.122467\n",
       "2020-01-23    0.207146\n",
       "2020-01-24    0.445541\n",
       "2020-01-25    0.469814\n",
       "2020-01-26    0.346325\n",
       "2020-01-27    0.467504\n",
       "2020-01-28    0.289589\n",
       "2020-01-29    0.471521\n",
       "2020-01-30    0.275306\n",
       "2020-01-31    0.265750\n",
       "2020-02-01    0.202877\n",
       "2020-02-02    0.214951\n",
       "2020-02-03    0.173614\n",
       "2020-02-04    0.195797\n",
       "2020-02-05    0.153820\n",
       "2020-02-06    0.184587\n",
       "2020-02-07    0.129208\n",
       "2020-02-08    0.126112\n",
       "2020-02-09    0.089335\n",
       "2020-02-10    0.097104\n",
       "2020-02-11    0.058521\n",
       "2020-02-12    0.079056\n",
       "2020-02-13    0.071111\n",
       "2020-02-14    0.228023\n",
       "2020-02-15    0.121475\n",
       "2020-02-16    0.025698\n",
       "2020-02-17    0.054713\n",
       "2020-02-18    0.020913\n",
       "2020-02-19    0.052874\n",
       "2020-02-20    0.015306\n",
       "2020-02-21    0.035050\n",
       "2020-02-22    0.000843\n",
       "2020-02-23    0.040551\n",
       "2020-02-24    0.007357\n",
       "2020-02-25    0.031184\n",
       "2020-02-26    0.002484\n",
       "2020-02-27    0.031212\n",
       "2020-02-28    0.007849\n",
       "2020-02-29    0.037950\n",
       "2020-03-01    0.014567\n",
       "2020-03-02    0.045396\n",
       "2020-03-03    0.019500\n",
       "2020-03-04    0.040767\n",
       "2020-03-05    0.024417\n",
       "2020-03-06    0.044427\n",
       "2020-03-07    0.027751\n",
       "2020-03-08    0.055106\n",
       "2020-03-09    0.036492\n",
       "2020-03-10    0.054154\n",
       "2020-03-11    0.036145\n",
       "2020-03-12    0.058821\n",
       "2020-03-13    0.052802\n",
       "2020-03-14    0.080987\n",
       "2020-03-15    0.065588\n",
       "2020-03-16    0.094253\n",
       "2020-03-17    0.075710\n",
       "2020-03-18    0.089725\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ARIMA拟合的其实是一阶差分ts_log_diff，predictions_ARIMA_diff[i]是第i个天与i-1个天的ts_log的差值。\n",
    "#由于差分化有一阶滞后，所以第一个天的数据是空的\n",
    "predictions_ARIMA_diff = pd.Series(results_ARIMA.fittedvalues, copy=True)\n",
    "predictions_ARIMA_diff#ARIMA拟合的一阶差分值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmedCount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>5.641907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>5.749393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>6.364751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>6.738152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>7.179308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>7.608374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>7.937732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>8.429236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>8.709630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>8.963928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>9.192278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>9.388319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>9.585415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>9.763478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>9.934308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>10.108589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>10.249769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>10.355804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>10.459554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>10.535078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-09</td>\n",
       "      <td>10.602468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>10.663358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>10.709718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>11.004098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>11.073738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>11.110954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>11.142499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>11.173683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>11.201770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>11.227082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>11.234124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>11.241143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>11.260250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-22</td>\n",
       "      <td>11.272572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>11.279757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>11.286953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>11.295105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>11.307204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>11.324111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>11.340784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>11.369078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>11.389367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>11.410981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>11.439064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>11.463536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>11.493620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>11.532277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>11.571345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>11.607654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>11.646451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>11.686080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>11.744466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>11.809535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>11.877784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>11.960383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>12.039794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>12.113990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>12.196921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>12.202029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmedCount\n",
       "日期                        \n",
       "2020-01-20        5.641907\n",
       "2020-01-21        5.749393\n",
       "2020-01-22        6.364751\n",
       "2020-01-23        6.738152\n",
       "2020-01-24        7.179308\n",
       "2020-01-25        7.608374\n",
       "2020-01-26        7.937732\n",
       "2020-01-27        8.429236\n",
       "2020-01-28        8.709630\n",
       "2020-01-29        8.963928\n",
       "2020-01-30        9.192278\n",
       "2020-01-31        9.388319\n",
       "2020-02-01        9.585415\n",
       "2020-02-02        9.763478\n",
       "2020-02-03        9.934308\n",
       "2020-02-04       10.108589\n",
       "2020-02-05       10.249769\n",
       "2020-02-06       10.355804\n",
       "2020-02-07       10.459554\n",
       "2020-02-08       10.535078\n",
       "2020-02-09       10.602468\n",
       "2020-02-10       10.663358\n",
       "2020-02-11       10.709718\n",
       "2020-02-12       11.004098\n",
       "2020-02-13       11.073738\n",
       "2020-02-14       11.110954\n",
       "2020-02-15       11.142499\n",
       "2020-02-16       11.173683\n",
       "2020-02-17       11.201770\n",
       "2020-02-18       11.227082\n",
       "2020-02-19       11.234124\n",
       "2020-02-20       11.241143\n",
       "2020-02-21       11.260250\n",
       "2020-02-22       11.272572\n",
       "2020-02-23       11.279757\n",
       "2020-02-24       11.286953\n",
       "2020-02-25       11.295105\n",
       "2020-02-26       11.307204\n",
       "2020-02-27       11.324111\n",
       "2020-02-28       11.340784\n",
       "2020-02-29       11.369078\n",
       "2020-03-01       11.389367\n",
       "2020-03-02       11.410981\n",
       "2020-03-03       11.439064\n",
       "2020-03-04       11.463536\n",
       "2020-03-05       11.493620\n",
       "2020-03-06       11.532277\n",
       "2020-03-07       11.571345\n",
       "2020-03-08       11.607654\n",
       "2020-03-09       11.646451\n",
       "2020-03-10       11.686080\n",
       "2020-03-11       11.744466\n",
       "2020-03-12       11.809535\n",
       "2020-03-13       11.877784\n",
       "2020-03-14       11.960383\n",
       "2020-03-15       12.039794\n",
       "2020-03-16       12.113990\n",
       "2020-03-17       12.196921\n",
       "2020-03-18       12.202029"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexedDataset_logScale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "日期\n",
      "2020-01-21    0.155787\n",
      "2020-01-22    0.278254\n",
      "2020-01-23    0.485399\n",
      "2020-01-24    0.930940\n",
      "2020-01-25    1.400754\n",
      "2020-01-26    1.747079\n",
      "2020-01-27    2.214583\n",
      "2020-01-28    2.504172\n",
      "2020-01-29    2.975693\n",
      "2020-01-30    3.250999\n",
      "2020-01-31    3.516749\n",
      "2020-02-01    3.719625\n",
      "2020-02-02    3.934577\n",
      "2020-02-03    4.108191\n",
      "2020-02-04    4.303988\n",
      "2020-02-05    4.457808\n",
      "2020-02-06    4.642395\n",
      "2020-02-07    4.771604\n",
      "2020-02-08    4.897716\n",
      "2020-02-09    4.987051\n",
      "2020-02-10    5.084154\n",
      "2020-02-11    5.142675\n",
      "2020-02-12    5.221731\n",
      "2020-02-13    5.292842\n",
      "2020-02-14    5.520866\n",
      "2020-02-15    5.642341\n",
      "2020-02-16    5.668040\n",
      "2020-02-17    5.722752\n",
      "2020-02-18    5.743666\n",
      "2020-02-19    5.796539\n",
      "2020-02-20    5.811845\n",
      "2020-02-21    5.846895\n",
      "2020-02-22    5.847738\n",
      "2020-02-23    5.888289\n",
      "2020-02-24    5.895646\n",
      "2020-02-25    5.926830\n",
      "2020-02-26    5.929314\n",
      "2020-02-27    5.960526\n",
      "2020-02-28    5.968375\n",
      "2020-02-29    6.006325\n",
      "2020-03-01    6.020892\n",
      "2020-03-02    6.066288\n",
      "2020-03-03    6.085788\n",
      "2020-03-04    6.126555\n",
      "2020-03-05    6.150971\n",
      "2020-03-06    6.195399\n",
      "2020-03-07    6.223149\n",
      "2020-03-08    6.278256\n",
      "2020-03-09    6.314748\n",
      "2020-03-10    6.368902\n",
      "2020-03-11    6.405047\n",
      "2020-03-12    6.463868\n",
      "2020-03-13    6.516669\n",
      "2020-03-14    6.597657\n",
      "2020-03-15    6.663245\n",
      "2020-03-16    6.757499\n",
      "2020-03-17    6.833208\n",
      "2020-03-18    6.922934\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Convert to cumulative sum 累计和\n",
    "#累加现有的diff，得到每个值与第一个天的差分（同log底的情况下）。\n",
    "#即predictions_ARIMA_diff_cumsum[i] 是第i个月与第1个月的ts_log的差值。\n",
    "predictions_ARIMA_diff_cumsum = predictions_ARIMA_diff.cumsum()\n",
    "\n",
    "print(predictions_ARIMA_diff_cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predictions_ARIMA_diff_cumsum = predictions_ARIMA_diff_cumsum.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "日期\n",
       "2020-01-20    5.641907\n",
       "2020-01-21    5.797694\n",
       "2020-01-22    5.920161\n",
       "2020-01-23    6.127306\n",
       "2020-01-24    6.572847\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#先ts_log_diff => ts_log=>ts_log => ts \n",
    "#先以ts_log的第一个值作为基数，复制给所有值，然后每个时刻的值累加与第一个月对应的差值(这样就解决了，第1、2个天diff数据为空的问题了)\n",
    "#然后得到了predictions_ARIMA_log => predictions_ARIMA\n",
    "predictions_ARIMA_log = pd.Series(indexedDataset_logScale['confirmedCount'].iloc[0], index=indexedDataset_logScale.index)\n",
    "predictions_ARIMA_log = predictions_ARIMA_log.add(predictions_ARIMA_diff_cumsum, fill_value=0)\n",
    "predictions_ARIMA_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x193e9ce8cc8>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inverse of log is exp.\n",
    "predictions_ARIMA = np.exp(predictions_ARIMA_log)\n",
    "#predictions_ARIMA\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "plt.plot(indexedDataset)\n",
    "plt.plot(predictions_ARIMA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We have 144(existing data of 12 yrs in months) data points. \n",
    "#And we want to forecast for additional 120 data points or 10 yrs.\n",
    "results_ARIMA.plot_predict(1,79) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on ARIMAResultsWrapper in module statsmodels.tsa.arima_model object:\n",
      "\n",
      "class ARIMAResultsWrapper(ARMAResultsWrapper)\n",
      " |  ARIMAResultsWrapper(results)\n",
      " |  \n",
      " |  Class which wraps a statsmodels estimation Results class and steps in to\n",
      " |  reattach metadata to results (if available)\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      ARIMAResultsWrapper\n",
      " |      ARMAResultsWrapper\n",
      " |      statsmodels.base.wrapper.ResultsWrapper\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  conf_int(self, alpha=0.05, cols=None, method='default')\n",
      " |      conf_int(self, alpha=0.05, cols=None, method='default')\n",
      " |      \n",
      " |      Returns the confidence interval of the fitted parameters.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      alpha : float, optional\n",
      " |          The significance level for the confidence interval.\n",
      " |          ie., The default `alpha` = .05 returns a 95% confidence interval.\n",
      " |      cols : array-like, optional\n",
      " |          `cols` specifies which confidence intervals to return\n",
      " |      method : string\n",
      " |          Not Implemented Yet\n",
      " |          Method to estimate the confidence_interval.\n",
      " |          \"Default\" : uses self.bse which is based on inverse Hessian for MLE\n",
      " |          \"hjjh\" :\n",
      " |          \"jac\" :\n",
      " |          \"boot-bse\"\n",
      " |          \"boot_quant\"\n",
      " |          \"profile\"\n",
      " |      \n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      conf_int : array\n",
      " |          Each row contains [lower, upper] limits of the confidence interval\n",
      " |          for the corresponding parameter. The first column contains all\n",
      " |          lower, the second column contains all upper limits.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> import statsmodels.api as sm\n",
      " |      >>> data = sm.datasets.longley.load(as_pandas=False)\n",
      " |      >>> data.exog = sm.add_constant(data.exog)\n",
      " |      >>> results = sm.OLS(data.endog, data.exog).fit()\n",
      " |      >>> results.conf_int()\n",
      " |      array([[-5496529.48322745, -1467987.78596704],\n",
      " |             [    -177.02903529,      207.15277984],\n",
      " |             [      -0.1115811 ,        0.03994274],\n",
      " |             [      -3.12506664,       -0.91539297],\n",
      " |             [      -1.5179487 ,       -0.54850503],\n",
      " |             [      -0.56251721,        0.460309  ],\n",
      " |             [     798.7875153 ,     2859.51541392]])\n",
      " |      \n",
      " |      \n",
      " |      >>> results.conf_int(cols=(2,3))\n",
      " |      array([[-0.1115811 ,  0.03994274],\n",
      " |             [-3.12506664, -0.91539297]])\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The confidence interval is based on the standard normal distribution.\n",
      " |      Models wish to use a different distribution should overwrite this\n",
      " |      method.\n",
      " |  \n",
      " |  cov_params(self)\n",
      " |      cov_params(self)\n",
      " |      None\n",
      " |  \n",
      " |  predict(self, start=None, end=None, exog=None, typ='linear', dynamic=False)\n",
      " |      predict(self, start=None, end=None, exog=None, typ='linear', dynamic=False)\n",
      " |      \n",
      " |      ARIMA model in-sample and out-of-sample prediction\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      \n",
      " |      start : int, str, or datetime\n",
      " |          Zero-indexed observation number at which to start forecasting, ie.,\n",
      " |          the first forecast is start. Can also be a date string to\n",
      " |          parse or a datetime type.\n",
      " |      end : int, str, or datetime\n",
      " |          Zero-indexed observation number at which to end forecasting, ie.,\n",
      " |          the first forecast is start. Can also be a date string to\n",
      " |          parse or a datetime type. However, if the dates index does not\n",
      " |          have a fixed frequency, end must be an integer index if you\n",
      " |          want out of sample prediction.\n",
      " |      exog : array-like, optional\n",
      " |          If the model is an ARMAX and out-of-sample forecasting is\n",
      " |          requested, exog must be given. Note that you'll need to pass\n",
      " |          `k_ar` additional lags for any exogenous variables. E.g., if you\n",
      " |          fit an ARMAX(2, q) model and want to predict 5 steps, you need 7\n",
      " |          observations to do this.\n",
      " |      dynamic : bool, optional\n",
      " |          The `dynamic` keyword affects in-sample prediction. If dynamic\n",
      " |          is False, then the in-sample lagged values are used for\n",
      " |          prediction. If `dynamic` is True, then in-sample forecasts are\n",
      " |          used in place of lagged dependent variables. The first forecasted\n",
      " |          value is `start`.\n",
      " |      typ : str {'linear', 'levels'}\n",
      " |      \n",
      " |          - 'linear' : Linear prediction in terms of the differenced\n",
      " |            endogenous variables.\n",
      " |          - 'levels' : Predict the levels of the original endogenous\n",
      " |            variables.\n",
      " |      \n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      predict : array\n",
      " |          The predicted values.\n",
      " |      \n",
      " |      \n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      It is recommended to use dates with the time-series models, as the\n",
      " |      below will probably make clear. However, if ARIMA is used without\n",
      " |      dates and/or `start` and `end` are given as indices, then these\n",
      " |      indices are in terms of the *original*, undifferenced series. Ie.,\n",
      " |      given some undifferenced observations::\n",
      " |      \n",
      " |       1970Q1, 1\n",
      " |       1970Q2, 1.5\n",
      " |       1970Q3, 1.25\n",
      " |       1970Q4, 2.25\n",
      " |       1971Q1, 1.2\n",
      " |       1971Q2, 4.1\n",
      " |      \n",
      " |      1970Q1 is observation 0 in the original series. However, if we fit an\n",
      " |      ARIMA(p,1,q) model then we lose this first observation through\n",
      " |      differencing. Therefore, the first observation we can forecast (if\n",
      " |      using exact MLE) is index 1. In the differenced series this is index\n",
      " |      0, but we refer to it as 1 from the original series.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from statsmodels.base.wrapper.ResultsWrapper:\n",
      " |  \n",
      " |  __dir__(self)\n",
      " |      Default dir() implementation.\n",
      " |  \n",
      " |  __getattribute__(self, attr)\n",
      " |      Return getattr(self, name).\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __init__(self, results)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  __setstate__(self, dict_)\n",
      " |  \n",
      " |  save(self, fname, remove_data=False)\n",
      " |      save a pickle of this instance\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      fname : string or filehandle\n",
      " |          fname can be a string to a file path or filename, or a filehandle.\n",
      " |      remove_data : bool\n",
      " |          If False (default), then the instance is pickled without changes.\n",
      " |          If True, then all arrays with length nobs are set to None before\n",
      " |          pickling. See the remove_data method.\n",
      " |          In some cases not all arrays will be set to None.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods inherited from statsmodels.base.wrapper.ResultsWrapper:\n",
      " |  \n",
      " |  load(fname) from builtins.type\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from statsmodels.base.wrapper.ResultsWrapper:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(results_ARIMA)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-01-21       329.538778\n",
      "2020-01-22       372.471572\n",
      "2020-01-23       458.200273\n",
      "2020-01-24       715.403767\n",
      "2020-01-25      1144.429454\n",
      "                  ...      \n",
      "2020-03-24    406368.234220\n",
      "2020-03-25    422596.453651\n",
      "2020-03-26    462061.463898\n",
      "2020-03-27    487695.028732\n",
      "2020-03-28    530574.099101\n",
      "Freq: D, Length: 68, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "predictions_ARIMA_=results_ARIMA.predict(1,68)#取68天的预测结果\n",
    "\n",
    "\n",
    "predictions_ARIMA_diff = pd.Series(predictions_ARIMA_, copy=True)\n",
    "predictions_ARIMA_diff#ARIMA拟合的一阶差分值\n",
    "predictions_ARIMA_diff_cumsum = predictions_ARIMA_diff.cumsum()\n",
    "\n",
    "\n",
    "#先ts_log_diff => ts_log=>ts_log => ts \n",
    "#先以ts_log的第一个值作为基数，复制给所有值，然后每个时刻的值累加与第一个月对应的差值(这样就解决了，第1、2个天diff数据为空的问题了)\n",
    "#然后得到了predictions_ARIMA_log => predictions_ARIMA\n",
    "predictions_ARIMA_log = pd.Series(indexedDataset_logScale['confirmedCount'].iloc[0], index=predictions_ARIMA_.index)\n",
    "predictions_ARIMA_log = predictions_ARIMA_log.add(predictions_ARIMA_diff_cumsum, fill_value=0)\n",
    "\n",
    "# Inverse of log is exp.\n",
    "predictions_ARIMA = np.exp(predictions_ARIMA_log)\n",
    "print(predictions_ARIMA)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Data1 = pd.read_csv('./world0120-0328.csv', index_col=0)#1月20日-3月28日 世界总确诊人数\n",
    "#增加id递增列\n",
    "#增加id递增列\n",
    "\n",
    "Data1 = Data1.reset_index()\n",
    "Data1['日期']= pd.to_datetime(Data1['日期'],infer_datetime_format=True) \n",
    "#set_index( ) 将 DataFrame 中的列转化为行索引\n",
    "indexedDataset_original = Data1.set_index(['日期'])\n",
    "\n",
    "#数据反转\n",
    "indexedDataset_original = indexedDataset_original.iloc[::-1]\n",
    "\n",
    "\n",
    "\n",
    "from matplotlib import font_manager\n",
    "\n",
    "my_font = font_manager.FontProperties(fname=\"C:/Windows/Fonts/simhei.ttf\")#黑体\n",
    "plt.figure(figsize=(10,8),dpi = 80)\n",
    "plt.xticks(rotation=45,fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "\n",
    "plt.plot(predictions_ARIMA,markersize=30,label='预测数据',color = 'blue',linewidth=2.0)#1月20日-3月28日 世界总确诊人数预测曲线\n",
    "plt.plot(indexedDataset_original,markersize=30,label='实际数据',color = 'orange',linewidth=2.0)#1月20日-3月28日 世界总确诊人数实际曲线\n",
    "plt.plot(indexedDataset,markersize=30,label='训练数据',color = 'green',linestyle='--',linewidth=2.0)#1月20日-3月18日 世界总确诊人数训练集曲线\n",
    "\n",
    "##添加描述信息\n",
    "plt.xlabel(\"时间日期\",fontproperties = my_font,fontsize=15)\n",
    "plt.ylabel(\"肺炎确诊总数\",fontproperties = my_font,fontsize=15)\n",
    "plt.title(\"1月20日至3月28日世界肺炎确诊人数趋势图\",fontproperties = my_font,fontsize=20)\n",
    "plt.grid(alpha=1) #网格线的透明程度  数值越大，网格颜色越深\n",
    "## 添加图例\n",
    "#############################待解决问题：为何我的图例 不会变大！！！！############################\n",
    "plt.legend(prop = my_font,loc=\"upper left\")\n",
    "#plt.savefig('figure.eps')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "https://www.bilibili.com/video/BV1Vp4y1C7Rr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "日期\n",
       "2020-01-21    0.155787\n",
       "2020-01-22    0.122467\n",
       "2020-01-23    0.207146\n",
       "2020-01-24    0.445541\n",
       "2020-01-25    0.469814\n",
       "2020-01-26    0.346325\n",
       "2020-01-27    0.467504\n",
       "2020-01-28    0.289589\n",
       "2020-01-29    0.471521\n",
       "2020-01-30    0.275306\n",
       "2020-01-31    0.265750\n",
       "2020-02-01    0.202877\n",
       "2020-02-02    0.214951\n",
       "2020-02-03    0.173614\n",
       "2020-02-04    0.195797\n",
       "2020-02-05    0.153820\n",
       "2020-02-06    0.184587\n",
       "2020-02-07    0.129208\n",
       "2020-02-08    0.126112\n",
       "2020-02-09    0.089335\n",
       "2020-02-10    0.097104\n",
       "2020-02-11    0.058521\n",
       "2020-02-12    0.079056\n",
       "2020-02-13    0.071111\n",
       "2020-02-14    0.228023\n",
       "2020-02-15    0.121475\n",
       "2020-02-16    0.025698\n",
       "2020-02-17    0.054713\n",
       "2020-02-18    0.020913\n",
       "2020-02-19    0.052874\n",
       "2020-02-20    0.015306\n",
       "2020-02-21    0.035050\n",
       "2020-02-22    0.000843\n",
       "2020-02-23    0.040551\n",
       "2020-02-24    0.007357\n",
       "2020-02-25    0.031184\n",
       "2020-02-26    0.002484\n",
       "2020-02-27    0.031212\n",
       "2020-02-28    0.007849\n",
       "2020-02-29    0.037950\n",
       "2020-03-01    0.014567\n",
       "2020-03-02    0.045396\n",
       "2020-03-03    0.019500\n",
       "2020-03-04    0.040767\n",
       "2020-03-05    0.024417\n",
       "2020-03-06    0.044427\n",
       "2020-03-07    0.027751\n",
       "2020-03-08    0.055106\n",
       "2020-03-09    0.036492\n",
       "2020-03-10    0.054154\n",
       "2020-03-11    0.036145\n",
       "2020-03-12    0.058821\n",
       "2020-03-13    0.052802\n",
       "2020-03-14    0.080987\n",
       "2020-03-15    0.065588\n",
       "2020-03-16    0.094253\n",
       "2020-03-17    0.075710\n",
       "2020-03-18    0.089725\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_ARIMA.fittedvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-21    0.155787\n",
       "2020-01-22    0.122467\n",
       "2020-01-23    0.207146\n",
       "2020-01-24    0.445541\n",
       "2020-01-25    0.469814\n",
       "                ...   \n",
       "2020-03-25    0.039158\n",
       "2020-03-26    0.089280\n",
       "2020-03-27    0.053992\n",
       "2020-03-28    0.084269\n",
       "2020-03-29    0.070034\n",
       "Freq: D, Length: 69, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_ARIMA.predict(1,69)"
   ]
  }
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