all finished :)

3 anos atrás · 2361a2b11f
--- a/COVID-19/Prediction/.vs/Prediction/v16/.suo
+++ b/COVID-19/Prediction/.vs/Prediction/v16/.suo
--- a/COVID-19/Prediction/Prediction/ARIMA.py
+++ b/COVID-19/Prediction/Prediction/ARIMA.py
@ -1,125 +0,0 @@
 import pandas as pd              
 import datetime as DT
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
 import math
 import statsmodels.api as sm
 from sklearn.metrics import mean_squared_error

 #打开数据文件
 dataset = pd.read_csv('E:\DaseIntro\COVID-19Analysis\COVID-19\covid-19-all.csv')

 #数据预处理
 def parse_ymd(s):
 	year_s, mon_s, day_s = s.split('-')
 	return datetime.datetime(int(year_s), int(mon_s), int(day_s)).strftime("%Y-%m-%d")
 dataset = dataset.fillna(0)
 dataset['Date'] = pd.to_datetime(dataset['Date'])
 dataset = dataset[['Country/Region','Confirmed','Recovered','Deaths','Date']].groupby(['Country/Region','Date']).sum().reset_index()

 #取出中、美的数据
 CN = dataset[dataset['Country/Region'] == 'China']
 CN.index = pd.Index(pd.date_range('2020-01-22','2020-12-09',freq = '1D'))  
 US = dataset[dataset['Country/Region'] == 'US']
 US.index = pd.Index(pd.date_range('2020-01-22','2020-12-09',freq = '1D'))  

 print(CN)
 print(US)

 #划分训练集、测试集
 trainCN = CN[CN['Date'] < '2020-11-01 ']  
 testCN = CN[CN['Date'] >= '2020-11-01']

 trainUS = US[US['Date'] < '2020-11-01 ']  
 testUS = US[US['Date'] >= '2020-11-01']

 #自回归移动平均模型（ARIMA）
 yCNARIMA = testCN.copy()
 yUSARIMA = testUS.copy()

 #训练模型
 fitCNconfirmed = sm.tsa.statespace.SARIMAX(trainCN['Confirmed'],trend='c').fit()
 fitCNrecovered = sm.tsa.statespace.SARIMAX(trainCN['Recovered'],trend='c').fit()
 fitCNdeaths = sm.tsa.statespace.SARIMAX(trainCN['Deaths'],trend='ct').fit()

 fitUSconfirmed = sm.tsa.statespace.SARIMAX(trainUS['Confirmed'],trend='ct').fit()
 fitUSrecovered = sm.tsa.statespace.SARIMAX(trainUS['Recovered'],trend='ct').fit()
 fitUSdeaths = sm.tsa.statespace.SARIMAX(trainUS['Deaths'],trend='ct').fit()

 #测试
 yCNARIMA['SARIMAconfirmed'] = fitCNconfirmed.predict(start="2020-11-01", end="2020-12-09", dynamic=True)
 yCNARIMA['SARIMArecovered'] = fitCNrecovered.predict(start="2020-11-01", end="2020-12-09", dynamic=True)
 yCNARIMA['SARIMAdeaths'] = fitCNdeaths.predict(start="2020-11-01", end="2020-12-09", dynamic=True)

 yUSARIMA['SARIMAconfirmed'] = fitUSconfirmed.predict(start="2020-11-01", end="2020-12-09")
 yUSARIMA['SARIMArecovered'] = fitUSrecovered.predict(start="2020-11-01", end="2020-12-09", dynamic=True)
 yUSARIMA['SARIMAdeaths'] = fitUSdeaths.predict(start="2020-11-01", end="2020-12-09", dynamic=True)

 #预测将来七天
 forecastCNARIMA = pd.DataFrame({'Date':['2020-12-10','2020-12-11','2020-12-12','2020-12-13','2020-12-14','2020-12-15','2020-12-16']})
 forecastUSARIMA = pd.DataFrame({'Date':['2020-12-10','2020-12-11','2020-12-12','2020-12-13','2020-12-14','2020-12-15','2020-12-16']})

 forecastCNARIMA['Date'] = pd.to_datetime(forecastCNARIMA['Date'], format='%Y/%m/%d').values.astype('datetime64[h]')  
 forecastCNARIMA['confirmedPred'] = fitCNconfirmed.predict(start="2020-12-10", end="2020-12-16", dynamic=True)
 forecastCNARIMA['recoveredPred'] = fitCNrecovered.predict(start="2020-12-10", end="2020-12-16", dynamic=True)
 forecastCNARIMA['deathsPred'] = fitCNdeaths.predict(start="2020-12-10", end="2020-12-16", dynamic=True)

 forecastUSARIMA['Date'] = pd.to_datetime(forecastUSARIMA['Date'], format='%Y/%m/%d').values.astype('datetime64[h]')  
 forecastUSARIMA['confirmedPred'] = fitUSconfirmed.predict(start="2020-12-10", end="2020-12-16", dynamic=True)
 forecastUSARIMA['recoveredPred'] = fitUSrecovered.predict(start="2020-12-10", end="2020-12-16", dynamic=True)
 forecastUSARIMA['deathsPred'] = fitUSdeaths.predict(start="2020-12-10", end="2020-12-16", dynamic=False)

 #RMSE
 rmseCNARIMACon = pow(mean_squared_error(np.asarray(testCN['Confirmed']), np.asarray(yCNARIMA['SARIMAconfirmed'])),0.05)
 rmseCNARIMARec = pow(mean_squared_error(np.asarray(testCN['Recovered']), np.asarray(yCNARIMA['SARIMArecovered'])),0.05)
 rmseCNARIMADea = pow(mean_squared_error(np.asarray(testCN['Deaths']), np.asarray(yCNARIMA['SARIMAdeaths'])),0.5)

 rmseUSARIMACon = pow(mean_squared_error(np.asarray(testUS['Confirmed']), np.asarray(yUSARIMA['SARIMAconfirmed'])),0.05)
 rmseUSARIMARec = pow(mean_squared_error(np.asarray(testUS['Recovered']), np.asarray(yUSARIMA['SARIMArecovered'])),0.05)
 rmseUSARIMADea = pow(mean_squared_error(np.asarray(testUS['Deaths']), np.asarray(yUSARIMA['SARIMAdeaths'])),0.05)

 #可视化
 fig = plt.figure()
 axCNARIMA = fig.add_subplot(211)
 axCNARIMA.set_title("ARIMA (CN)",verticalalignment="bottom",fontsize="13")

 CN.index = pd.Index(pd.date_range('2020-01-22','2020-12-09',freq = '1D'))  
 yCNARIMA.index = pd.Index(pd.date_range('2020-11-01','2020-12-09',freq = '1D'))  
 forecastCNARIMA.index = pd.Index(pd.date_range('2020-12-10','2020-12-16',freq = '1D'))  

 axCNARIMA.plot(CN['Confirmed'],label="confirmed",linestyle=":")
 axCNARIMA.plot(CN['Recovered'],label="recovered",linestyle=":")
 axCNARIMA.plot(CN['Deaths'],label="deaths",linestyle=":")

 axCNARIMA.plot(yCNARIMA['SARIMAconfirmed'],label="confirmed test")
 axCNARIMA.plot(yCNARIMA['SARIMArecovered'],label="recovered test")
 axCNARIMA.plot(yCNARIMA['SARIMAdeaths'],label="deaths test")

 axCNARIMA.plot(forecastCNARIMA['confirmedPred'],label="confirmed prediction")
 axCNARIMA.plot(forecastCNARIMA['recoveredPred'],label="recovered prediction")
 axCNARIMA.plot(forecastCNARIMA['deathsPred'],label="deaths prediction")

 axUSARIMA = fig.add_subplot(212)
 axUSARIMA.set_title("ARIMA (US)",verticalalignment="bottom",fontsize="13")

 US.index = pd.Index(pd.date_range('2020-01-22','2020-12-09',freq = '1D'))  
 yUSARIMA.index = pd.Index(pd.date_range('2020-11-01','2020-12-09',freq = '1D'))  
 forecastUSARIMA.index = pd.Index(pd.date_range('2020-12-10','2020-12-16',freq = '1D'))  

 axUSARIMA.plot(US['Confirmed'],label="confirmed",linestyle=":")
 axUSARIMA.plot(US['Recovered'],label="recovered",linestyle=":")
 axUSARIMA.plot(US['Deaths'],label="deaths",linestyle=":")

 axUSARIMA.plot(yUSARIMA['SARIMAconfirmed'],label="confirmed test")
 axUSARIMA.plot(yUSARIMA['SARIMArecovered'],label="recovered test")
 axUSARIMA.plot(yUSARIMA['SARIMAdeaths'],label="deaths test")

 axUSARIMA.plot(forecastUSARIMA['confirmedPred'],label="confirmed prediction")
 axUSARIMA.plot(forecastUSARIMA['recoveredPred'],label="recovered prediction")
 axUSARIMA.plot(forecastUSARIMA['deathsPred'],label="deaths prediction")

 plt.tight_layout()
 plt.gcf().autofmt_xdate()
 plt.legend(labelspacing=0.05)
 plt.show()
--- a/COVID-19/Prediction/Prediction/ARIMA.pyproj
+++ b/COVID-19/Prediction/Prediction/ARIMA.pyproj
@ -23,8 +23,12 @@
    <EnableUnmanagedDebugging>false</EnableUnmanagedDebugging>
  </PropertyGroup>
  <ItemGroup>
    <Compile Include="ARIMA.py" />
    <Compile Include="StabilityTest.py">
    <Compile Include="CN.py">
      <SubType>Code</SubType>
    </Compile>
    <Compile Include="CNDeaPara .py" />
    <Compile Include="CNRecPara.py" />
    <Compile Include="CNConPara.py">
      <SubType>Code</SubType>
    </Compile>
  </ItemGroup>
--- a/COVID-19/Prediction/Prediction/CN.py
+++ b/COVID-19/Prediction/Prediction/CN.py
@ -0,0 +1,96 @@
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 import statsmodels.api as sm
 from statsmodels.tsa.stattools import adfuller
 from statsmodels.tsa.arima_model import ARIMA
 from pmdarima import auto_arima

 #打开数据文件
 dataset = pd.read_csv('E:\DaseIntro\COVID-19Analysis\COVID-19\covid-19-all.csv')

 #数据预处理
 def parse_ymd(s):
 	year_s, mon_s, day_s = s.split('-')
 	return datetime.datetime(int(year_s), int(mon_s), int(day_s)).strftime("%Y-%m-%d")
 dataset = dataset.fillna(0)
 dataset['Date'] = pd.to_datetime(dataset['Date'])
 dataset = dataset[['Country/Region','Confirmed','Recovered','Deaths','Date']].groupby(['Country/Region','Date']).sum().reset_index()
 CN = dataset[dataset['Country/Region'] == 'China']
 CN.index = pd.Index(pd.date_range('2020-01-22','2020-12-09',freq = '1D'))  

 #差分后可视化
 fig1 = plt.figure()
 axcon = fig1.add_subplot(221)
 axcon.set_title("Confirmed")
 confirmedSeries = pd.DataFrame(CN['Confirmed'])
 confirmedSeries = confirmedSeries.fillna(0)
 confirmedSeries['Confirmed'] = confirmedSeries['Confirmed'] - confirmedSeries['Confirmed'].shift(1)
 axcon.plot(confirmedSeries)

 axrec = fig1.add_subplot(222)
 axrec.set_title("Recovered")
 recoveredSeries = pd.DataFrame(CN['Recovered'])
 recoveredSeries = recoveredSeries.fillna(0)
 recoveredSeries['Recovered'] = recoveredSeries['Recovered'] - recoveredSeries['Recovered'].shift(1)
 axrec.plot(recoveredSeries)

 axdea = fig1.add_subplot(223)
 axdea.set_title("Deaths")
 deathsSeries = pd.DataFrame(CN['Deaths'])
 deathsSeries = deathsSeries.fillna(0)
 deathsSeries['Deaths'] = deathsSeries['Deaths'] - deathsSeries['Deaths'].shift(1)
 axdea.plot(deathsSeries)
 plt.show()

 #ADF检验
 print(sm.tsa.stattools.adfuller(confirmedSeries.iloc[1:]))
 print(sm.tsa.stattools.adfuller(recoveredSeries.iloc[1:]))
 print(sm.tsa.stattools.adfuller(deathsSeries.iloc[1:]))

 #ARIMA模型
 modelConfirmed = sm.tsa.ARIMA(confirmedSeries.iloc[1:],(1,0,2)).fit()
 confirmed_pre=modelConfirmed.predict(start="2020-01-23", end="2020-12-31", dynamic = False)
 modelRecovered = sm.tsa.ARIMA(recoveredSeries.iloc[1:],(1,0,2)).fit()
 recovered_pre=modelRecovered.predict(start="2020-01-23", end="2020-12-31", dynamic = False)
 modelDeaths = sm.tsa.ARIMA(deathsSeries.iloc[1:],(1,0,1)).fit()
 deaths_pre=modelDeaths.predict(start="2020-01-23", end="2020-12-31", dynamic = False)

 #逆差分还原
 temp=np.array(CN['Confirmed'])
 for i in range(len(temp)):
 	confirmed_pre[i]+=temp[i]
 for i in range(len(temp),confirmed_pre.shape[0]):
 	confirmed_pre[i]+=confirmed_pre[i-1]
 confirmed_pre=pd.DataFrame({'confirmed_pre':confirmed_pre})
 confirmed_pre.index = pd.Index(pd.date_range('2020-01-23','2020-12-31',freq = '1D'))  

 temp=np.array(CN['Recovered'])
 for i in range(len(temp)):
 	recovered_pre[i]+=temp[i]
 for i in range(len(temp),confirmed_pre.shape[0]):
 	recovered_pre[i]+=recovered_pre[i-1]
 recovered_pre=pd.DataFrame({'recovered_pre':recovered_pre})
 recovered_pre.index = pd.Index(pd.date_range('2020-01-23','2020-12-31',freq = '1D'))  

 temp=np.array(CN['Deaths'])
 for i in range(len(temp)):
 	deaths_pre[i]+=temp[i]
 for i in range(len(temp),deaths_pre.shape[0]):
 	deaths_pre[i]+=deaths_pre[i-1]
 deaths_pre=pd.DataFrame({'deaths_pre':deaths_pre})
 deaths_pre.index = pd.Index(pd.date_range('2020-01-23','2020-12-31',freq = '1D'))  

 #可视化
 fig2 = plt.figure()
 axcon = fig2.add_subplot(311)
 axcon.plot(CN['Confirmed'],label="confirmed")
 axcon.plot(confirmed_pre,label="ARIMA")
 axrec = fig2.add_subplot(312)
 axrec.plot(CN['Recovered'],label="recovered")
 axrec.plot(recovered_pre,label="ARIMA")
 axdea = fig2.add_subplot(313)
 axdea.plot(CN['Deaths'],label="deaths")
 axdea.plot(deaths_pre,label="ARIMA")
 plt.legend()
 plt.show()
--- a/COVID-19/Prediction/Prediction/CNConPara.py
+++ b/COVID-19/Prediction/Prediction/CNConPara.py
@ -0,0 +1,18 @@
 import matplotlib.pyplot as plt
 import numpy as np

 AIC = np.zeros(14)
 BIC = np.zeros(14)
 AIC = [5351.5918, 5335.5662, 5323.8356, 5311.2017, 5273.9674, 5272.4677, 5272.1961, 5300.3335, 5273.3238, 5272.9836, 5275.0035, 5283.7308, 5271.9971, 5276.3102]
 BIC = [5362.9155, 5350.6644, 5342.7083, 5322.5254, 5289.0656, 5291.3404, 5294.8434, 5315.4317, 5292.1965, 5295.6309, 5301.4254, 5302.6036, 5294.6445, 5302.7321]

 x = ['0,0,1','0,0,2','0,0,3','1,0,0','1,0,1','1,0,2','1,0,3','2,0,0','2,0,1','2,0,2','2,0,3','3,0,0','3,0,1','3,0,2']

 plt.title("AIC/BIC of CN ARIMA Confirmed Model")
 plt.plot(x,AIC,label="AIC")
 plt.plot(x,BIC,label="BIC")
 for y in [AIC, BIC]:
    for x1, yy in zip(x, y):
        plt.text(x1, yy + 1, str(yy), ha='center', va='bottom', fontsize=10, rotation=0)
 plt.legend()
 plt.show()
--- a/COVID-19/Prediction/Prediction/CNDeaPara
+++ b/COVID-19/Prediction/Prediction/CNDeaPara
@ -0,0 +1,18 @@
 import matplotlib.pyplot as plt
 import numpy as np

 AIC = np.zeros(13)
 BIC = np.zeros(13)
 AIC = [3720.6474, 3719.8167, 3720.4415, 3720.2469, 3709.0862, 3710.7921, 3712.7911, 3718.7009, 3710.7933, 3712.7608, 3714.7807, 3718.7941, 3712.7926]
 BIC = [3731.9711, 3734.9149, 3739.3143, 3731.5706, 3724.1844, 3729.6649, 3735.4384, 3733.7991, 3729.6661, 3735.4081, 3741.2026, 3737.6668, 3735.4399]

 x = ['0,0,1','0,0,2','0,0,3','1,0,0','1,0,1','1,0,2','1,0,3','2,0,0','2,0,1','2,0,2','2,0,3','3,0,0','3,0,1']

 plt.title("AIC/BIC of CN ARIMA Deaths Model")
 plt.plot(x,AIC,label="AIC")
 plt.plot(x,BIC,label="BIC")
 for y in [AIC, BIC]:
    for x1, yy in zip(x, y):
        plt.text(x1, yy + 1, str(yy), ha='center', va='bottom', fontsize=10, rotation=0)
 plt.legend()
 plt.show()
--- a/COVID-19/Prediction/Prediction/CNRecPara.py
+++ b/COVID-19/Prediction/Prediction/CNRecPara.py
@ -0,0 +1,18 @@
 import matplotlib.pyplot as plt
 import numpy as np

 AIC = np.zeros(14)
 BIC = np.zeros(14)
 AIC = [4883.7687, 4768.7267, 4665.6302, 4613.9799, 4476.1181, 4406.9768, 4408.8600, 4462.9301, 4434.4957, 4408.8190, 4410.7338, 4418.0734, 4419.9409, 4409.4140]
 BIC = [4895.0924, 4783.8249, 4684.5030, 4625.3036, 4491.2163, 4425.8495, 4431.5073, 4478.0283, 4453.3685, 4431.4663, 4437.1557, 4436.9461, 4442.5882, 4435.8358]

 x = ['0,0,1','0,0,2','0,0,3','1,0,0','1,0,1','1,0,2','1,0,3','2,0,0','2,0,1','2,0,2','2,0,3','3,0,0','3,0,1','3,0,2']

 plt.title("AIC/BIC of CN ARIMA Recovered Model")
 plt.plot(x,AIC,label="AIC")
 plt.plot(x,BIC,label="BIC")
 for y in [AIC, BIC]:
    for x1, yy in zip(x, y):
        plt.text(x1, yy + 1, str(yy), ha='center', va='bottom', fontsize=10, rotation=0)
 plt.legend()
 plt.show()
--- a/COVID-19/Prediction/Prediction/Holt_Winters.py
+++ b/COVID-19/Prediction/Prediction/Holt_Winters.py
@ -18,99 +18,58 @@ dataset = dataset.fillna(0)
 dataset['Date'] = pd.to_datetime(dataset['Date'])
 dataset = dataset[['Country/Region','Confirmed','Recovered','Deaths','Date']].groupby(['Country/Region','Date']).sum().reset_index()

 #取出中、美的数据
 #取出中国数据
 CN = dataset[dataset['Country/Region'] == 'China']
 CN.index = pd.Index(pd.date_range('2020-01-22','2020-12-09',freq = '1D'))  
 US = dataset[dataset['Country/Region'] == 'US']
 US.index = pd.Index(pd.date_range('2020-01-22','2020-12-09',freq = '1D'))  

 #划分训练集、测试集
 trainCN = CN[CN['Date'] < '2020-11-01 ']  
 testCN = CN[CN['Date'] >= '2020-11-01']

 trainUS = US[US['Date'] < '2020-11-01 ']  
 testUS = US[US['Date'] >= '2020-11-01']

 #简单指数法
 yCNexp = testCN.copy()
 yUSexp = testUS.copy()

 #训练模型
 confirmedCNexp = ExponentialSmoothing(np.asarray(trainCN['Confirmed']), trend='add', seasonal=None).fit()  
 recoveredCNexp = ExponentialSmoothing(np.asarray(trainCN['Recovered']), trend='add', seasonal=None).fit()  
 deathsCNexp = ExponentialSmoothing(np.asarray(trainCN['Deaths']), trend='add', seasonal=None).fit()  

 confirmedUSexp = ExponentialSmoothing(np.asarray(trainUS['Confirmed']), trend='add', seasonal=None).fit()  
 recoveredUSexp = ExponentialSmoothing(np.asarray(trainUS['Recovered']), trend='add', seasonal=None).fit()  
 deathsUSexp = ExponentialSmoothing(np.asarray(trainUS['Deaths']), trend='add', seasonal=None).fit()  
 #测试
 yCNexp['confirmedTest'] = confirmedCNexp.forecast(len(testCN))
 yCNexp['recoveredTest'] = recoveredCNexp.forecast(len(testCN))
 yCNexp['deathsTest'] = deathsCNexp.forecast(len(testCN))

 yUSexp['confirmedTest'] = confirmedUSexp.forecast(len(testUS))
 yUSexp['recoveredTest'] = recoveredUSexp.forecast(len(testUS))
 yUSexp['deathsTest'] = deathsUSexp.forecast(len(testUS))
 #预测将来七天
 forecastCNexp = pd.DataFrame({'Date':['2020-12-10','2020-12-11','2020-12-12','2020-12-13','2020-12-14','2020-12-15','2020-12-16']})
 forecastUSexp = pd.DataFrame({'Date':['2020-12-10','2020-12-11','2020-12-12','2020-12-13','2020-12-14','2020-12-15','2020-12-16']})

 forecastCNexp['Date'] = pd.to_datetime(forecastCNexp['Date'], format='%Y/%m/%d').values.astype('datetime64[h]')  
 forecastCNexp['confirmedPred'] = confirmedCNexp.forecast(len(forecastCNexp))
 forecastCNexp['recoveredPred'] = recoveredCNexp.forecast(len(forecastCNexp))
 forecastCNexp['deathsPred'] = deathsCNexp.forecast(len(forecastCNexp))

 forecastUSexp['Date'] = pd.to_datetime(forecastUSexp['Date'], format='%Y/%m/%d').values.astype('datetime64[h]')  
 forecastUSexp['confirmedPred'] = confirmedUSexp.forecast(len(forecastUSexp))
 forecastUSexp['recoveredPred'] = recoveredUSexp.forecast(len(forecastUSexp))
 forecastUSexp['deathsPred'] = deathsUSexp.forecast(len(forecastUSexp))
 #RMSE
 rmseCNexpCon = pow(mean_squared_error(np.asarray(testCN['Confirmed']), np.asarray(yCNexp['confirmedTest'])),0.05)
 rmseCNexpRec = pow(mean_squared_error(np.asarray(testCN['Recovered']), np.asarray(yCNexp['recoveredTest'])),0.05)
 rmseCNexpDea = pow(mean_squared_error(np.asarray(testCN['Deaths']), np.asarray(yCNexp['deathsTest'])),0.5)

 rmseUSexpCon = pow(mean_squared_error(np.asarray(testUS['Confirmed']), np.asarray(yUSexp['confirmedTest'])),0.05)
 rmseUSexpRec = pow(mean_squared_error(np.asarray(testUS['Recovered']), np.asarray(yUSexp['recoveredTest'])),0.05)
 rmseUSexpDea = pow(mean_squared_error(np.asarray(testUS['Deaths']), np.asarray(yUSexp['deathsTest'])),0.05)
 #可视化
 fig = plt.figure()
 axCNexp = fig.add_subplot(211)
 axCNexp.set_title("Holt-Winters (CN)",verticalalignment="bottom",fontsize="13")
 plt.title("Holt-Winters",verticalalignment="bottom",fontsize="13")

 CN.index = pd.Index(pd.date_range('2020-01-22','2020-12-09',freq = '1D'))  
 yCNexp.index = pd.Index(pd.date_range('2020-11-01','2020-12-09',freq = '1D'))  
 forecastCNexp.index = pd.Index(pd.date_range('2020-12-10','2020-12-16',freq = '1D'))  

 axCNexp.plot(CN['Confirmed'],label="confirmed",linestyle=":")
 axCNexp.plot(CN['Recovered'],label="recovered",linestyle=":")
 axCNexp.plot(CN['Deaths'],label="deaths",linestyle=":")

 axCNexp.plot(yCNexp['confirmedTest'],label="confirmed test")
 axCNexp.plot(yCNexp['recoveredTest'],label="recovered test")
 axCNexp.plot(yCNexp['deathsTest'],label="deaths test")

 axCNexp.plot(forecastCNexp['confirmedPred'],label="confirmed prediction")
 axCNexp.plot(forecastCNexp['recoveredPred'],label="recovered prediction")
 axCNexp.plot(forecastCNexp['deathsPred'],label="deaths prediction")

 axUSexp = fig.add_subplot(212)
 axUSexp.set_title("Holt-Winters (US)",verticalalignment="bottom",fontsize="13")

 US.index = pd.Index(pd.date_range('2020-01-22','2020-12-09',freq = '1D'))  
 yUSexp.index = pd.Index(pd.date_range('2020-11-01','2020-12-09',freq = '1D'))  
 forecastUSexp.index = pd.Index(pd.date_range('2020-12-10','2020-12-16',freq = '1D'))  

 axUSexp.plot(US['Confirmed'],label="confirmed",linestyle=":")
 axUSexp.plot(US['Recovered'],label="recovered",linestyle=":")
 axUSexp.plot(US['Deaths'],label="deaths",linestyle=":")

 axUSexp.plot(yUSexp['confirmedTest'],label="confirmed test")
 axUSexp.plot(yUSexp['recoveredTest'],label="recovered test")
 axUSexp.plot(yUSexp['deathsTest'],label="deaths test")
 plt.plot(CN['Confirmed'],label="confirmed",linestyle=":")
 plt.plot(CN['Recovered'],label="recovered",linestyle=":")
 plt.plot(CN['Deaths'],label="deaths",linestyle=":")

 axUSexp.plot(forecastUSexp['confirmedPred'],label="confirmed prediction")
 axUSexp.plot(forecastUSexp['recoveredPred'],label="recovered prediction")
 axUSexp.plot(forecastUSexp['deathsPred'],label="deaths prediction")
 plt.plot(yCNexp['confirmedTest'],label="confirmed test")
 plt.plot(yCNexp['recoveredTest'],label="recovered test")
 plt.plot(yCNexp['deathsTest'],label="deaths test")

 plt.plot(forecastCNexp['confirmedPred'],label="confirmed prediction")
 plt.plot(forecastCNexp['recoveredPred'],label="recovered prediction")
 plt.plot(forecastCNexp['deathsPred'],label="deaths prediction")
 plt.tight_layout()
 plt.gcf().autofmt_xdate()
 plt.legend(labelspacing=0.05)
--- a/COVID-19/Prediction/Prediction/StabilityTest.py
+++ b/COVID-19/Prediction/Prediction/StabilityTest.py
@ -1,38 +0,0 @@
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 import statsmodels.api as sm
 from statsmodels.graphics.tsaplots import acf,pacf,plot_acf,plot_pacf
 from statsmodels.tsa.arima_model import ARMA
 from statsmodels.tsa.stattools import adfuller

 #打开数据文件
 dataset = pd.read_csv('E:\DaseIntro\COVID-19Analysis\COVID-19\covid-19-all.csv')

 #数据预处理
 def parse_ymd(s):
 	year_s, mon_s, day_s = s.split('-')
 	return datetime.datetime(int(year_s), int(mon_s), int(day_s)).strftime("%Y-%m-%d")
 dataset = dataset.fillna(0)
 dataset['Date'] = pd.to_datetime(dataset['Date'])
 dataset = dataset[['Country/Region','Confirmed','Recovered','Deaths','Date']].groupby(['Country/Region','Date']).sum().reset_index()

 #取出中、美的数据
 CN = dataset[dataset['Country/Region'] == 'China']
 CN.index = pd.Index(pd.date_range('2020-01-22','2020-12-09',freq = '1D'))  
 US = dataset[dataset['Country/Region'] == 'US']
 US.index = pd.Index(pd.date_range('2020-01-22','2020-12-09',freq = '1D'))

 #检验Confirmed
 CNconfirmedSeries = pd.DataFrame(CN['Confirmed'])
 CNconfirmedSeries['Confirmed'] = CNconfirmedSeries['Confirmed'] - CNconfirmedSeries['Confirmed'].shift(1)
 CNconfirmedSeries.plot(figsize=(8,6))

 CNrecoveredSeries = pd.DataFrame(CN['Recovered'])
 CNrecoveredSeries['Recovered'] = CNrecoveredSeries['Recovered'] - CNrecoveredSeries['Recovered'].shift(1)
 CNrecoveredSeries.plot(figsize=(8,6))

 CNdeathsSeries = pd.DataFrame(CN['Deaths'])
 CNdeathsSeries['Deaths'] = CNdeathsSeries['Deaths'] - CNdeathsSeries['Deaths'].shift(1)
 CNdeathsSeries.plot(figsize=(8,6))
 plt.show()
--- a/COVID-19/Visualization/.vs/Visualization/v16/.suo
+++ b/COVID-19/Visualization/.vs/Visualization/v16/.suo
--- a/COVID-19/Visualization/Project/China.html
+++ b/COVID-19/Visualization/Project/China.html
@ -8,11 +8,11 @@

 </head>
 <body>
    <div id="2994d21d5f1e472d9bd72a8e63405c9d" class="chart-container" style="width:900px; height:500px;"></div>
    <div id="b0fd2915225446dc993103826838ac2e" class="chart-container" style="width:900px; height:500px;"></div>
    <script>
        var chart_2994d21d5f1e472d9bd72a8e63405c9d = echarts.init(
            document.getElementById('2994d21d5f1e472d9bd72a8e63405c9d'), 'white', {renderer: 'canvas'});
        var option_2994d21d5f1e472d9bd72a8e63405c9d = {
        var chart_b0fd2915225446dc993103826838ac2e = echarts.init(
            document.getElementById('b0fd2915225446dc993103826838ac2e'), 'white', {renderer: 'canvas'});
        var option_b0fd2915225446dc993103826838ac2e = {
    "animation": true,
    "animationThreshold": 2000,
    "animationDuration": 1000,
@ -261,7 +261,7 @@
        "borderWidth": 0
    }
 };
        chart_2994d21d5f1e472d9bd72a8e63405c9d.setOption(option_2994d21d5f1e472d9bd72a8e63405c9d);
        chart_b0fd2915225446dc993103826838ac2e.setOption(option_b0fd2915225446dc993103826838ac2e);
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 </body>
 </html>
--- a/COVID-19/Visualization/Project/Visualization.py
+++ b/COVID-19/Visualization/Project/Visualization.py
@ -11,7 +11,7 @@ from pyecharts import options
 plt.rcParams['axes.unicode_minus'] = False   

 #打开数据文件
 dataset = pd.read_csv('E:\dase intro\COVID-19Analysis\COVID-19\covid-19-all.csv')
 dataset = pd.read_csv('E:\DaseIntro\COVID-19Analysis\COVID-19\covid-19-all.csv')

 #数据预处理
 def parse_ymd(s):
--- a/COVID-19/Visualization/Project/world.html
+++ b/COVID-19/Visualization/Project/world.html
@ -8,11 +8,11 @@

 </head>
 <body>
    <div id="35dd4be7e6ea4b96915f75571e27271d" class="chart-container" style="width:900px; height:500px;"></div>
    <div id="be6aa75c8c074717a723e4836e7cd56a" class="chart-container" style="width:900px; height:500px;"></div>
    <script>
        var chart_35dd4be7e6ea4b96915f75571e27271d = echarts.init(
            document.getElementById('35dd4be7e6ea4b96915f75571e27271d'), 'white', {renderer: 'canvas'});
        var option_35dd4be7e6ea4b96915f75571e27271d = {
        var chart_be6aa75c8c074717a723e4836e7cd56a = echarts.init(
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        var option_be6aa75c8c074717a723e4836e7cd56a = {
    "animation": true,
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    "animationDuration": 1000,
@ -51,7 +51,7 @@
        {
            "type": "map",
            "label": {
                "show": false,
                "show": true,
                "position": "top",
                "margin": 8
            },
@ -1001,7 +1001,7 @@
        "borderWidth": 0
    }
 };
        chart_35dd4be7e6ea4b96915f75571e27271d.setOption(option_35dd4be7e6ea4b96915f75571e27271d);
        chart_be6aa75c8c074717a723e4836e7cd56a.setOption(option_be6aa75c8c074717a723e4836e7cd56a);
    </script>
 </body>
 </html>