essay done

COVID-19/Prediction/Prediction/CN.py

@@ -109,11 +109,13 @@ fig2 = plt.figure()
 axcon = fig2.add_subplot(311)
 axcon.plot(CN['Confirmed'],label="confirmed")
 axcon.plot(confirmed_pre,label="ARIMA")
+axcon.legend()
 axrec = fig2.add_subplot(312)
 axrec.plot(CN['Recovered'],label="recovered")
 axrec.plot(recovered_pre,label="ARIMA")
+axrec.legend()
 axdea = fig2.add_subplot(313)
 axdea.plot(CN['Deaths'],label="deaths")
 axdea.plot(deaths_pre,label="ARIMA")
-plt.legend()
+axdea.legend()
 plt.show()

COVID-19/Prediction/Prediction/CNConPara.py

@@ -8,7 +8,7 @@ BIC = [5362.9155, 5350.6644, 5342.7083, 5322.5254, 5289.0656, 5291.3404, 5294.84
 
 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.title("AIC/BIC of CN ARIMA Confirmed Model",fontsize="25")
 plt.plot(x,AIC,label="AIC")
 plt.plot(x,BIC,label="BIC")
 for y in [AIC, BIC]:

COVID-19/Prediction/Prediction/CNDeaPara .py

@@ -8,7 +8,7 @@ BIC = [3731.9711, 3734.9149, 3739.3143, 3731.5706, 3724.1844, 3729.6649, 3735.43
 
 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.title("AIC/BIC of CN ARIMA Deaths Model",fontsize='25')
 plt.plot(x,AIC,label="AIC")
 plt.plot(x,BIC,label="BIC")
 for y in [AIC, BIC]:

COVID-19/Prediction/Prediction/CNRecPara.py

@@ -8,7 +8,7 @@ BIC = [4895.0924, 4783.8249, 4684.5030, 4625.3036, 4491.2163, 4425.8495, 4431.50
 
 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.title("AIC/BIC of CN ARIMA Recovered Model",fontsize='25')
 plt.plot(x,AIC,label="AIC")
 plt.plot(x,BIC,label="BIC")
 for y in [AIC, BIC]:

COVID-19/Prediction/Prediction/ExponentialSmoothing.py

@@ -48,8 +48,8 @@ forecastCNexp['recoveredPred'] = recoveredCNexp.forecast(len(forecastCNexp))
 forecastCNexp['deathsPred'] = deathsCNexp.forecast(len(forecastCNexp))
 
 #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)
+rmseCNexpCon = pow(mean_squared_error(np.asarray(testCN['Confirmed']), np.asarray(yCNexp['confirmedTest'])),0.5)
+rmseCNexpRec = pow(mean_squared_error(np.asarray(testCN['Recovered']), np.asarray(yCNexp['recoveredTest'])),0.5)
 rmseCNexpDea = pow(mean_squared_error(np.asarray(testCN['Deaths']), np.asarray(yCNexp['deathsTest'])),0.5)
 
 print(rmseCNexpCon)
@@ -58,7 +58,7 @@ print(rmseCNexpDea)
 
 
 #可视化
-plt.title("Holt-Winters",verticalalignment="bottom",fontsize="13")
+plt.title("Exponential Smoothing",verticalalignment="bottom",fontsize="20")
 
 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'))