From 32a001e08e6c1171dc8d14b4a214d53715317c14 Mon Sep 17 00:00:00 2001
From: 10195501441 <10195501441@stu.ecnu.edu.cn>
Date: Thu, 31 Dec 2020 13:09:06 +0800
Subject: [PATCH] added ARIMA method

---
 COVID-19/Prediction/.vs/Prediction/v16/.suo        | Bin 34304 -> 24576 bytes
 COVID-19/Prediction/Prediction.sln                 |   2 +-
 COVID-19/Prediction/Prediction/ARIMA.py            | 122 +++++++++++++++++++++
 COVID-19/Prediction/Prediction/ARIMA.pyproj        |  37 +++++++
 .../Prediction/Prediction/Holt_Linear_Trend.py     | 122 ---------------------
 .../Prediction/Prediction/Holt_Linear_Trend.pyproj |  37 -------
 6 files changed, 160 insertions(+), 160 deletions(-)
 create mode 100644 COVID-19/Prediction/Prediction/ARIMA.py
 create mode 100644 COVID-19/Prediction/Prediction/ARIMA.pyproj
 delete mode 100644 COVID-19/Prediction/Prediction/Holt_Linear_Trend.py
 delete mode 100644 COVID-19/Prediction/Prediction/Holt_Linear_Trend.pyproj

diff --git a/COVID-19/Prediction/.vs/Prediction/v16/.suo b/COVID-19/Prediction/.vs/Prediction/v16/.suo
index 3be7b6d49fffb7df4c32f24a6c55f72c3c24e555..5b02e307b985df8523fc621dc7ebc8ecac45be8e 100644
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diff --git a/COVID-19/Prediction/Prediction.sln b/COVID-19/Prediction/Prediction.sln
index 2298e0b..07e0c3e 100644
--- a/COVID-19/Prediction/Prediction.sln
+++ b/COVID-19/Prediction/Prediction.sln
@@ -5,7 +5,7 @@ VisualStudioVersion = 16.0.30804.86
 MinimumVisualStudioVersion = 10.0.40219.1
 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}"
+Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "ARIMA", "Prediction\ARIMA.pyproj", "{3F8A3B3F-C79E-412C-8EE4-DA8FF5DB31A6}"
 EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
diff --git a/COVID-19/Prediction/Prediction/ARIMA.py b/COVID-19/Prediction/Prediction/ARIMA.py
new file mode 100644
index 0000000..6564e8f
--- /dev/null
+++ b/COVID-19/Prediction/Prediction/ARIMA.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'],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
new file mode 100644
index 0000000..4c5722a
--- /dev/null
+++ b/COVID-19/Prediction/Prediction/ARIMA.pyproj
@@ -0,0 +1,37 @@
+<Project DefaultTargets="Build" xmlns="http://schemas.microsoft.com/developer/msbuild/2003" ToolsVersion="4.0">
+  <PropertyGroup>
+    <Configuration Condition=" '$(Configuration)' == '' ">Debug</Configuration>
+    <SchemaVersion>2.0</SchemaVersion>
+    <ProjectGuid>3f8a3b3f-c79e-412c-8ee4-da8ff5db31a6</ProjectGuid>
+    <ProjectHome>
+    </ProjectHome>
+    <StartupFile>
+    </StartupFile>
+    <SearchPath>
+    </SearchPath>
+    <WorkingDirectory>.</WorkingDirectory>
+    <OutputPath>.</OutputPath>
+    <Name>ARIMA</Name>
+    <RootNamespace>ARIMA</RootNamespace>
+  </PropertyGroup>
+  <PropertyGroup Condition=" '$(Configuration)' == 'Debug' ">
+    <DebugSymbols>true</DebugSymbols>
+    <EnableUnmanagedDebugging>false</EnableUnmanagedDebugging>
+  </PropertyGroup>
+  <PropertyGroup Condition=" '$(Configuration)' == 'Release' ">
+    <DebugSymbols>true</DebugSymbols>
+    <EnableUnmanagedDebugging>false</EnableUnmanagedDebugging>
+  </PropertyGroup>
+  <ItemGroup>
+    <Compile Include="ARIMA.py" />
+  </ItemGroup>
+  <Import Project="$(MSBuildExtensionsPath32)\Microsoft\VisualStudio\v$(VisualStudioVersion)\Python Tools\Microsoft.PythonTools.targets" />
+  <!-- Uncomment the CoreCompile target to enable the Build command in
+       Visual Studio and specify your pre- and post-build commands in
+       the BeforeBuild and AfterBuild targets below. -->
+  <!--<Target Name="CoreCompile" />-->
+  <Target Name="BeforeBuild">
+  </Target>
+  <Target Name="AfterBuild">
+  </Target>
+</Project>
\ No newline at end of file
diff --git a/COVID-19/Prediction/Prediction/Holt_Linear_Trend.py b/COVID-19/Prediction/Prediction/Holt_Linear_Trend.py
deleted file mode 100644
index 85826ae..0000000
--- a/COVID-19/Prediction/Prediction/Holt_Linear_Trend.py
+++ /dev/null
@@ -1,122 +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:\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
deleted file mode 100644
index c05dbd0..0000000
--- a/COVID-19/Prediction/Prediction/Holt_Linear_Trend.pyproj
+++ /dev/null
@@ -1,37 +0,0 @@
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