all finished :)

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()

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>

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()

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()

COVID-19/Prediction/Prediction/CNDeaPara .py

@@ -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()

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()

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)

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()

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);
     </script>
 </body>
 </html>

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):

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(
+            document.getElementById('be6aa75c8c074717a723e4836e7cd56a'), 'white', {renderer: 'canvas'});
+        var option_be6aa75c8c074717a723e4836e7cd56a = {
     "animation": true,
     "animationThreshold": 2000,
     "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>