Holt-Winters

COVID-19/Prediction/Prediction/Prediction.py

@@ -1 +1,64 @@
+import pandas as pd              
+import datetime as DT
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+import math
+from statsmodels.tsa.api import SimpleExpSmoothing
+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、俄的数据
+CN = dataset[dataset['Country/Region'] == 'China'].reset_index()
+CN = CN.drop('index', 1)
+US = dataset[dataset['Country/Region'] == 'US'].reset_index()
+US = US.drop('index', 1)
+RUS = dataset[dataset['Country/Region'] == 'Russia'].reset_index()
+RUS = RUS.drop('index', 1)
+
+#中国
+
+#划分训练集、测试集
+trainCN = CN[CN['Date'] < '2020-11-01 ']  
+testCN = CN[CN['Date'] >= '2020-11-01']  
+
+#简单指数法
+yCNexp = testCN.copy()
+confirmedCNexp = SimpleExpSmoothing(np.asarray(trainCN['Confirmed'])).fit(smoothing_level=0.4, optimized=False)
+recoveredCNexp = SimpleExpSmoothing(np.asarray(trainCN['Recovered'])).fit(smoothing_level=0.4, optimized=False)
+deathsCNexp = SimpleExpSmoothing(np.asarray(trainCN['Deaths'])).fit(smoothing_level=0.4, optimized=False)
+yCNexp['confirmedTest'] = confirmedCNexp.forecast(len(testCN))
+yCNexp['recoveredTest'] = recoveredCNexp.forecast(len(testCN))
+yCNexp['deathsTest'] = deathsCNexp.forecast(len(testCN))
+
+#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)
+
+#可视化
+figCN = plt.figure()
+axCNexp = figCN.add_subplot(311)
+axCNexp.set_title("Simple Exponential Smoothing(CN)",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'))  
+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="exp confirmed")
+axCNexp.plot(yCNexp['recoveredTest'],label="exp recovered")
+axCNexp.plot(yCNexp['deathsTest'],label="exp deaths")
+plt.tight_layout()
+plt.gcf().autofmt_xdate()
+plt.legend()
+plt.show()