diff --git a/COVID-19/Prediction/.vs/Prediction/v16/.suo b/COVID-19/Prediction/.vs/Prediction/v16/.suo index 2a77487..3be7b6d 100644 Binary files a/COVID-19/Prediction/.vs/Prediction/v16/.suo and b/COVID-19/Prediction/.vs/Prediction/v16/.suo differ diff --git a/COVID-19/Prediction/Prediction.sln b/COVID-19/Prediction/Prediction.sln index 67423c5..2298e0b 100644 --- a/COVID-19/Prediction/Prediction.sln +++ b/COVID-19/Prediction/Prediction.sln @@ -3,7 +3,9 @@ Microsoft Visual Studio Solution File, Format Version 12.00 # Visual Studio Version 16 VisualStudioVersion = 16.0.30804.86 MinimumVisualStudioVersion = 10.0.40219.1 -Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "Prediction", "Prediction\Prediction.pyproj", "{FE4194E6-8A95-4B93-968D-ED4305D7F944}" +Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "Holt_Winters", "Prediction\Holt_Winters.pyproj", "{FE4194E6-8A95-4B93-968D-ED4305D7F944}" +EndProject +Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "Holt_Linear_Trend", "Prediction\Holt_Linear_Trend.pyproj", "{3F8A3B3F-C79E-412C-8EE4-DA8FF5DB31A6}" EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution @@ -13,6 +15,8 @@ Global GlobalSection(ProjectConfigurationPlatforms) = postSolution {FE4194E6-8A95-4B93-968D-ED4305D7F944}.Debug|Any CPU.ActiveCfg = Debug|Any CPU {FE4194E6-8A95-4B93-968D-ED4305D7F944}.Release|Any CPU.ActiveCfg = Release|Any CPU + {3F8A3B3F-C79E-412C-8EE4-DA8FF5DB31A6}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {3F8A3B3F-C79E-412C-8EE4-DA8FF5DB31A6}.Release|Any CPU.ActiveCfg = Release|Any CPU EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE diff --git a/COVID-19/Prediction/Prediction/Holt_Linear_Trend.py b/COVID-19/Prediction/Prediction/Holt_Linear_Trend.py new file mode 100644 index 0000000..85826ae --- /dev/null +++ b/COVID-19/Prediction/Prediction/Holt_Linear_Trend.py @@ -0,0 +1,122 @@ +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:\dase intro\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')) + +#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).fit() +fitCNrecovered = sm.tsa.statespace.SARIMAX(trainCN['Recovered']).fit() +fitCNdeaths = sm.tsa.statespace.SARIMAX(trainCN['Deaths']).fit() + +fitUSconfirmed = sm.tsa.statespace.SARIMAX(trainUS.Confirmed,trend='ct').fit() +fitUSrecovered = sm.tsa.statespace.SARIMAX(trainUS['Recovered']).fit() +fitUSdeaths = sm.tsa.statespace.SARIMAX(trainUS['Deaths']).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=True) + +#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() \ No newline at end of file diff --git a/COVID-19/Prediction/Prediction/Holt_Linear_Trend.pyproj b/COVID-19/Prediction/Prediction/Holt_Linear_Trend.pyproj new file mode 100644 index 0000000..c05dbd0 --- /dev/null +++ b/COVID-19/Prediction/Prediction/Holt_Linear_Trend.pyproj @@ -0,0 +1,37 @@ + + + Debug + 2.0 + 3f8a3b3f-c79e-412c-8ee4-da8ff5db31a6 + + + + + + + . + . + Holt_Linear_Trend + ARIMA + + + true + false + + + true + false + + + + + + + + + + + + \ No newline at end of file diff --git a/COVID-19/Prediction/Prediction/Prediction.py b/COVID-19/Prediction/Prediction/Holt_Winters.py similarity index 99% rename from COVID-19/Prediction/Prediction/Prediction.py rename to COVID-19/Prediction/Prediction/Holt_Winters.py index b48c9fd..404db3c 100644 --- a/COVID-19/Prediction/Prediction/Prediction.py +++ b/COVID-19/Prediction/Prediction/Holt_Winters.py @@ -18,7 +18,7 @@ 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、俄的数据 +#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'] diff --git a/COVID-19/Prediction/Prediction/Prediction.pyproj b/COVID-19/Prediction/Prediction/Holt_Winters.pyproj similarity index 89% rename from COVID-19/Prediction/Prediction/Prediction.pyproj rename to COVID-19/Prediction/Prediction/Holt_Winters.pyproj index 6e425c5..85c7ea9 100644 --- a/COVID-19/Prediction/Prediction/Prediction.pyproj +++ b/COVID-19/Prediction/Prediction/Holt_Winters.pyproj @@ -3,13 +3,14 @@ Debug 2.0 fe4194e6-8a95-4b93-968d-ed4305d7f944 - . - Prediction.py + + + Holt_Winters.py . . - Prediction + Holt_Winters Prediction @@ -21,7 +22,7 @@ false - +