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@ -4,7 +4,7 @@ import numpy as np |
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import matplotlib.pyplot as plt |
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import matplotlib.dates as mdates |
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import math |
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from statsmodels.tsa.api import SimpleExpSmoothing |
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from statsmodels.tsa.api import ExponentialSmoothing |
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from sklearn.metrics import mean_squared_error |
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#打开数据文件 |
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@ -19,46 +19,99 @@ dataset['Date'] = pd.to_datetime(dataset['Date']) |
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dataset = dataset[['Country/Region','Confirmed','Recovered','Deaths','Date']].groupby(['Country/Region','Date']).sum().reset_index() |
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#取出中、美、俄的数据 |
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CN = dataset[dataset['Country/Region'] == 'China'].reset_index() |
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CN = CN.drop('index', 1) |
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US = dataset[dataset['Country/Region'] == 'US'].reset_index() |
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US = US.drop('index', 1) |
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RUS = dataset[dataset['Country/Region'] == 'Russia'].reset_index() |
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RUS = RUS.drop('index', 1) |
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#中国 |
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CN = dataset[dataset['Country/Region'] == 'China'] |
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CN.index = pd.Index(pd.date_range('2020-01-22','2020-12-09',freq = '1D')) |
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US = dataset[dataset['Country/Region'] == 'US'] |
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US.index = pd.Index(pd.date_range('2020-01-22','2020-12-09',freq = '1D')) |
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#划分训练集、测试集 |
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trainCN = CN[CN['Date'] < '2020-11-01 '] |
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testCN = CN[CN['Date'] >= '2020-11-01'] |
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testCN = CN[CN['Date'] >= '2020-11-01'] |
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trainUS = US[US['Date'] < '2020-11-01 '] |
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testUS = US[US['Date'] >= '2020-11-01'] |
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#简单指数法 |
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yCNexp = testCN.copy() |
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confirmedCNexp = SimpleExpSmoothing(np.asarray(trainCN['Confirmed'])).fit(smoothing_level=0.4, optimized=False) |
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recoveredCNexp = SimpleExpSmoothing(np.asarray(trainCN['Recovered'])).fit(smoothing_level=0.4, optimized=False) |
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deathsCNexp = SimpleExpSmoothing(np.asarray(trainCN['Deaths'])).fit(smoothing_level=0.4, optimized=False) |
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yUSexp = testUS.copy() |
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#训练模型 |
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confirmedCNexp = ExponentialSmoothing(np.asarray(trainCN['Confirmed']), trend='add', seasonal=None).fit() |
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recoveredCNexp = ExponentialSmoothing(np.asarray(trainCN['Recovered']), trend='add', seasonal=None).fit() |
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deathsCNexp = ExponentialSmoothing(np.asarray(trainCN['Deaths']), trend='add', seasonal=None).fit() |
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confirmedUSexp = ExponentialSmoothing(np.asarray(trainUS['Confirmed']), trend='add', seasonal=None).fit() |
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recoveredUSexp = ExponentialSmoothing(np.asarray(trainUS['Recovered']), trend='add', seasonal=None).fit() |
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deathsUSexp = ExponentialSmoothing(np.asarray(trainUS['Deaths']), trend='add', seasonal=None).fit() |
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#测试 |
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yCNexp['confirmedTest'] = confirmedCNexp.forecast(len(testCN)) |
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yCNexp['recoveredTest'] = recoveredCNexp.forecast(len(testCN)) |
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yCNexp['deathsTest'] = deathsCNexp.forecast(len(testCN)) |
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yUSexp['confirmedTest'] = confirmedUSexp.forecast(len(testUS)) |
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yUSexp['recoveredTest'] = recoveredUSexp.forecast(len(testUS)) |
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yUSexp['deathsTest'] = deathsUSexp.forecast(len(testUS)) |
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#预测将来七天 |
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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']}) |
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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']}) |
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forecastCNexp['Date'] = pd.to_datetime(forecastCNexp['Date'], format='%Y/%m/%d').values.astype('datetime64[h]') |
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forecastCNexp['confirmedPred'] = confirmedCNexp.forecast(len(forecastCNexp)) |
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forecastCNexp['recoveredPred'] = recoveredCNexp.forecast(len(forecastCNexp)) |
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forecastCNexp['deathsPred'] = deathsCNexp.forecast(len(forecastCNexp)) |
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forecastUSexp['Date'] = pd.to_datetime(forecastUSexp['Date'], format='%Y/%m/%d').values.astype('datetime64[h]') |
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forecastUSexp['confirmedPred'] = confirmedUSexp.forecast(len(forecastUSexp)) |
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forecastUSexp['recoveredPred'] = recoveredUSexp.forecast(len(forecastUSexp)) |
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forecastUSexp['deathsPred'] = deathsUSexp.forecast(len(forecastUSexp)) |
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#RMSE |
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rmseCNexpCon = pow(mean_squared_error(np.asarray(testCN['Confirmed']), np.asarray(yCNexp['confirmedTest'])),0.05) |
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rmseCNexpRec = pow(mean_squared_error(np.asarray(testCN['Recovered']), np.asarray(yCNexp['recoveredTest'])),0.05) |
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rmseCNexpDea = pow(mean_squared_error(np.asarray(testCN['Deaths']), np.asarray(yCNexp['deathsTest'])),0.5) |
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rmseUSexpCon = pow(mean_squared_error(np.asarray(testUS['Confirmed']), np.asarray(yUSexp['confirmedTest'])),0.05) |
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rmseUSexpRec = pow(mean_squared_error(np.asarray(testUS['Recovered']), np.asarray(yUSexp['recoveredTest'])),0.05) |
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rmseUSexpDea = pow(mean_squared_error(np.asarray(testUS['Deaths']), np.asarray(yUSexp['deathsTest'])),0.05) |
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#可视化 |
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figCN = plt.figure() |
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axCNexp = figCN.add_subplot(311) |
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axCNexp.set_title("Simple Exponential Smoothing(CN)",verticalalignment="bottom",fontsize="13") |
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fig = plt.figure() |
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axCNexp = fig.add_subplot(211) |
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axCNexp.set_title("Holt-Winters (CN)",verticalalignment="bottom",fontsize="13") |
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CN.index = pd.Index(pd.date_range('2020-01-22','2020-12-09',freq = '1D')) |
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yCNexp.index = pd.Index(pd.date_range('2020-11-01','2020-12-09',freq = '1D')) |
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forecastCNexp.index = pd.Index(pd.date_range('2020-12-10','2020-12-16',freq = '1D')) |
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axCNexp.plot(CN['Confirmed'],label="confirmed",linestyle=":") |
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axCNexp.plot(CN['Recovered'],label="recovered",linestyle=":") |
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axCNexp.plot(CN['Deaths'],label="deaths",linestyle=":") |
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axCNexp.plot(yCNexp['confirmedTest'],label="exp confirmed") |
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axCNexp.plot(yCNexp['recoveredTest'],label="exp recovered") |
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axCNexp.plot(yCNexp['deathsTest'],label="exp deaths") |
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axCNexp.plot(yCNexp['confirmedTest'],label="confirmed test") |
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axCNexp.plot(yCNexp['recoveredTest'],label="recovered test") |
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axCNexp.plot(yCNexp['deathsTest'],label="deaths test") |
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axCNexp.plot(forecastCNexp['confirmedPred'],label="confirmed prediction") |
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axCNexp.plot(forecastCNexp['recoveredPred'],label="recovered prediction") |
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axCNexp.plot(forecastCNexp['deathsPred'],label="deaths prediction") |
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axUSexp = fig.add_subplot(212) |
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axUSexp.set_title("Holt-Winters (US)",verticalalignment="bottom",fontsize="13") |
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US.index = pd.Index(pd.date_range('2020-01-22','2020-12-09',freq = '1D')) |
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yUSexp.index = pd.Index(pd.date_range('2020-11-01','2020-12-09',freq = '1D')) |
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forecastUSexp.index = pd.Index(pd.date_range('2020-12-10','2020-12-16',freq = '1D')) |
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axUSexp.plot(US['Confirmed'],label="confirmed",linestyle=":") |
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axUSexp.plot(US['Recovered'],label="recovered",linestyle=":") |
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axUSexp.plot(US['Deaths'],label="deaths",linestyle=":") |
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axUSexp.plot(yUSexp['confirmedTest'],label="confirmed test") |
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axUSexp.plot(yUSexp['recoveredTest'],label="recovered test") |
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axUSexp.plot(yUSexp['deathsTest'],label="deaths test") |
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axUSexp.plot(forecastUSexp['confirmedPred'],label="confirmed prediction") |
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axUSexp.plot(forecastUSexp['recoveredPred'],label="recovered prediction") |
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axUSexp.plot(forecastUSexp['deathsPred'],label="deaths prediction") |
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plt.tight_layout() |
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plt.gcf().autofmt_xdate() |
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plt.legend() |
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plt.legend(labelspacing=0.05) |
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plt.show() |
