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COVID-19Analysis
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all finished :)

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杨浩然 4 years ago
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      COVID-19/Prediction/.vs/Prediction/v16/.suo
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      COVID-19/Prediction/Prediction/ARIMA.py
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      COVID-19/Prediction/Prediction/ARIMA.pyproj
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      COVID-19/Prediction/Prediction/CN.py
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      COVID-19/Prediction/Prediction/CNConPara.py
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      COVID-19/Prediction/Prediction/CNDeaPara .py
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      COVID-19/Prediction/Prediction/CNRecPara.py
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      COVID-19/Prediction/Prediction/Holt_Winters.py
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      COVID-19/Prediction/Prediction/StabilityTest.py
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COVID-19/Prediction/.vs/Prediction/v16/.suo View File


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COVID-19/Prediction/Prediction/ARIMA.py View File

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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()

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COVID-19/Prediction/Prediction/ARIMA.pyproj View File

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<EnableUnmanagedDebugging>false</EnableUnmanagedDebugging> <EnableUnmanagedDebugging>false</EnableUnmanagedDebugging>
</PropertyGroup> </PropertyGroup>
<ItemGroup> <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> <SubType>Code</SubType>
</Compile> </Compile>
</ItemGroup> </ItemGroup>

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COVID-19/Prediction/Prediction/CN.py View File

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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()

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COVID-19/Prediction/Prediction/CNConPara.py View File

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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()

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COVID-19/Prediction/Prediction/CNDeaPara .py View File

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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()

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COVID-19/Prediction/Prediction/CNRecPara.py View File

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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()

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COVID-19/Prediction/Prediction/Holt_Winters.py View File

@ -18,99 +18,58 @@ dataset = dataset.fillna(0)
dataset['Date'] = pd.to_datetime(dataset['Date']) dataset['Date'] = pd.to_datetime(dataset['Date'])
dataset = dataset[['Country/Region','Confirmed','Recovered','Deaths','Date']].groupby(['Country/Region','Date']).sum().reset_index() 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 = dataset[dataset['Country/Region'] == 'China']
CN.index = pd.Index(pd.date_range('2020-01-22','2020-12-09',freq = '1D')) 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 #划分训练集、测试集
trainCN = CN[CN['Date'] < '2020-11-01 '] trainCN = CN[CN['Date'] < '2020-11-01 ']
testCN = 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 #简单指数法
yCNexp = testCN.copy() yCNexp = testCN.copy()
yUSexp = testUS.copy()
#训练模型 #训练模型
confirmedCNexp = ExponentialSmoothing(np.asarray(trainCN['Confirmed']), trend='add', seasonal=None).fit() confirmedCNexp = ExponentialSmoothing(np.asarray(trainCN['Confirmed']), trend='add', seasonal=None).fit()
recoveredCNexp = ExponentialSmoothing(np.asarray(trainCN['Recovered']), 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() 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 #测试
yCNexp['confirmedTest'] = confirmedCNexp.forecast(len(testCN)) yCNexp['confirmedTest'] = confirmedCNexp.forecast(len(testCN))
yCNexp['recoveredTest'] = recoveredCNexp.forecast(len(testCN)) yCNexp['recoveredTest'] = recoveredCNexp.forecast(len(testCN))
yCNexp['deathsTest'] = deathsCNexp.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 #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']}) 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['Date'] = pd.to_datetime(forecastCNexp['Date'], format='%Y/%m/%d').values.astype('datetime64[h]')
forecastCNexp['confirmedPred'] = confirmedCNexp.forecast(len(forecastCNexp)) forecastCNexp['confirmedPred'] = confirmedCNexp.forecast(len(forecastCNexp))
forecastCNexp['recoveredPred'] = recoveredCNexp.forecast(len(forecastCNexp)) forecastCNexp['recoveredPred'] = recoveredCNexp.forecast(len(forecastCNexp))
forecastCNexp['deathsPred'] = deathsCNexp.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 #RMSE
rmseCNexpCon = pow(mean_squared_error(np.asarray(testCN['Confirmed']), np.asarray(yCNexp['confirmedTest'])),0.05) 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) 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) 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 #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')) 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')) 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')) 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.tight_layout()
plt.gcf().autofmt_xdate() plt.gcf().autofmt_xdate()
plt.legend(labelspacing=0.05) plt.legend(labelspacing=0.05)

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- 38
COVID-19/Prediction/Prediction/StabilityTest.py View File

@ -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()

BIN
COVID-19/Visualization/.vs/Visualization/v16/.suo View File


+ 5
- 5
COVID-19/Visualization/Project/China.html View File

@ -8,11 +8,11 @@
</head> </head>
<body> <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> <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, "animation": true,
"animationThreshold": 2000, "animationThreshold": 2000,
"animationDuration": 1000, "animationDuration": 1000,
@ -261,7 +261,7 @@
"borderWidth": 0 "borderWidth": 0
} }
}; };
chart_2994d21d5f1e472d9bd72a8e63405c9d.setOption(option_2994d21d5f1e472d9bd72a8e63405c9d);
chart_b0fd2915225446dc993103826838ac2e.setOption(option_b0fd2915225446dc993103826838ac2e);
</script> </script>
</body> </body>
</html> </html>

+ 1
- 1
COVID-19/Visualization/Project/Visualization.py View File

@ -11,7 +11,7 @@ from pyecharts import options
plt.rcParams['axes.unicode_minus'] = False plt.rcParams['axes.unicode_minus'] = False
#打开数据文件 #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 #数据预处理
def parse_ymd(s): def parse_ymd(s):

+ 6
- 6
COVID-19/Visualization/Project/world.html View File

@ -8,11 +8,11 @@
</head> </head>
<body> <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> <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, "animation": true,
"animationThreshold": 2000, "animationThreshold": 2000,
"animationDuration": 1000, "animationDuration": 1000,
@ -51,7 +51,7 @@
{ {
"type": "map", "type": "map",
"label": { "label": {
"show": false,
"show": true,
"position": "top", "position": "top",
"margin": 8 "margin": 8
}, },
@ -1001,7 +1001,7 @@
"borderWidth": 0 "borderWidth": 0
} }
}; };
chart_35dd4be7e6ea4b96915f75571e27271d.setOption(option_35dd4be7e6ea4b96915f75571e27271d);
chart_be6aa75c8c074717a723e4836e7cd56a.setOption(option_be6aa75c8c074717a723e4836e7cd56a);
</script> </script>
</body> </body>
</html> </html>

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