diff --git a/COVID-19/Prediction/.vs/Prediction/v16/.suo b/COVID-19/Prediction/.vs/Prediction/v16/.suo index 7001ed3..edfed0d 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/ARIMA.py b/COVID-19/Prediction/Prediction/ARIMA.py deleted file mode 100644 index d07b6bb..0000000 --- a/COVID-19/Prediction/Prediction/ARIMA.py +++ /dev/null @@ -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() \ No newline at end of file diff --git a/COVID-19/Prediction/Prediction/ARIMA.pyproj b/COVID-19/Prediction/Prediction/ARIMA.pyproj index 0a538fc..bb21875 100644 --- a/COVID-19/Prediction/Prediction/ARIMA.pyproj +++ b/COVID-19/Prediction/Prediction/ARIMA.pyproj @@ -23,8 +23,12 @@ false - - + + Code + + + + Code diff --git a/COVID-19/Prediction/Prediction/CN.py b/COVID-19/Prediction/Prediction/CN.py new file mode 100644 index 0000000..4888244 --- /dev/null +++ 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() \ No newline at end of file diff --git a/COVID-19/Prediction/Prediction/CNConPara.py b/COVID-19/Prediction/Prediction/CNConPara.py new file mode 100644 index 0000000..635336a --- /dev/null +++ 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() \ No newline at end of file diff --git a/COVID-19/Prediction/Prediction/CNDeaPara .py b/COVID-19/Prediction/Prediction/CNDeaPara .py new file mode 100644 index 0000000..5c4f6b3 --- /dev/null +++ b/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() \ No newline at end of file diff --git a/COVID-19/Prediction/Prediction/CNRecPara.py b/COVID-19/Prediction/Prediction/CNRecPara.py new file mode 100644 index 0000000..14ea810 --- /dev/null +++ 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() \ No newline at end of file diff --git a/COVID-19/Prediction/Prediction/Holt_Winters.py b/COVID-19/Prediction/Prediction/Holt_Winters.py index 47c7621..75e8a31 100644 --- 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) diff --git a/COVID-19/Prediction/Prediction/StabilityTest.py b/COVID-19/Prediction/Prediction/StabilityTest.py deleted file mode 100644 index dbc742f..0000000 --- a/COVID-19/Prediction/Prediction/StabilityTest.py +++ /dev/null @@ -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() \ No newline at end of file diff --git a/COVID-19/Visualization/.vs/Visualization/v16/.suo b/COVID-19/Visualization/.vs/Visualization/v16/.suo index 312217d..9c5afaf 100644 Binary files a/COVID-19/Visualization/.vs/Visualization/v16/.suo and b/COVID-19/Visualization/.vs/Visualization/v16/.suo differ diff --git a/COVID-19/Visualization/Project/China.html b/COVID-19/Visualization/Project/China.html index a9ee0b1..5e05b6a 100644 --- a/COVID-19/Visualization/Project/China.html +++ b/COVID-19/Visualization/Project/China.html @@ -8,11 +8,11 @@ -
+
diff --git a/COVID-19/Visualization/Project/Visualization.py b/COVID-19/Visualization/Project/Visualization.py index 6a99791..b0c4a82 100644 --- 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): diff --git a/COVID-19/Visualization/Project/world.html b/COVID-19/Visualization/Project/world.html index c2d7e9d..5351272 100644 --- a/COVID-19/Visualization/Project/world.html +++ b/COVID-19/Visualization/Project/world.html @@ -8,11 +8,11 @@ -
+