For循环中的排序模型
For循环中的排序模型
提问于 2016-11-24 22:01:12
假设我在R中有下面的"for“循环来生成滚动预测，并从一组四种ARMA模型中重构模型。我构建的代码是基于上一篇关于这个主题的文章(参见下面的链接：https://stats.stackexchange.com/questions/208985/selecting-arima-order-using-rolling-forecast)
h <- 1
train <- window(USDlogreturns, end=1162)
test <- window(USDlogreturns, start=1163)
n <- length(test) - h + 1
fit1 <- Arima(train, order=c(0,0,0), include.mean=TRUE, method="ML")
fit2 <- Arima(train, order=c(0,0,1), include.mean=TRUE, method="ML")
fit3 <- Arima(train, order=c(1,0,0), include.mean=TRUE, method="ML")
fit4 <- Arima(train, order=c(1,0,1), include.mean=TRUE, method="ML")
fc1 <- ts(numeric(n), start=1163+1, freq=1)
fc2 <- ts(numeric(n), start=1163+1, freq=1)
fc3 <- ts(numeric(n), start=1163+1, freq=1)
fc4 <- ts(numeric(n), start=1163+1, freq=1)
for(i in 1:n)
{
x <- window(USDlogreturns, end=1162 + i)
refit1 <- Arima(x, model=fit1, include.mean=TRUE, method="ML")
refit2 <- Arima(x, model=fit2, include.mean=TRUE, method="ML")
refit3 <- Arima(x, model=fit3, include.mean=TRUE, method="ML")
refit4 <- Arima(x, model=fit4, include.mean=TRUE, method="ML")
fc1[i] <- forecast(refit1, h=h)$mean[h]
fc2[i] <- forecast(refit2, h=h)$mean[h]
fc3[i] <- forecast(refit3, h=h)$mean[h]
fc4[i] <- forecast(refit4, h=h)$mean[h]
}
result.fc<-cbind(fc1, fc2, fc3, fc4)以下代码计算各种预测精度度量(有关这些度量的说明，请参阅此链接：http://127.0.0.1:15135/library/forecast/html/accuracy.html)。
accuracy(fc1, test)[,1:5]
accuracy(fc2, test)[,1:5]
accuracy(fc3, test)[,1:5]
accuracy(fc4, test)[,1:5]我的问题是：
我如何告诉循环按照五个不同的矩阵中的五个预测精度度量对四个估计模型进行排序？
谢谢你的帮助。
Stack Overflow用户
回答已采纳
发布于 2016-11-25 14:28:54
我的解决方案，但我不知道我是否正确理解，你想得到什么结果。
# empty vectors
acc_fc1=c()
acc_fc2=c()
acc_fc3=c()
acc_fc4=c()
h <- 1
train <- window(USDlogreturns, end=1162)
test <- window(USDlogreturns, start=1163)
n <- length(test) - h + 1
fit1 <- Arima(train, order=c(0,0,0), include.mean=TRUE, method="ML")
fit2 <- Arima(train, order=c(0,0,1), include.mean=TRUE, method="ML")
fit3 <- Arima(train, order=c(1,0,0), include.mean=TRUE, method="ML")
fit4 <- Arima(train, order=c(1,0,1), include.mean=TRUE, method="ML")
fc1 <- ts(numeric(n), start=1163+1, freq=1)
fc2 <- ts(numeric(n), start=1163+1, freq=1)
fc3 <- ts(numeric(n), start=1163+1, freq=1)
fc4 <- ts(numeric(n), start=1163+1, freq=1)
for(i in 1:n)
{
x <- window(USDlogreturns, end=1162 + i)
refit1 <- Arima(x, model=fit1, include.mean=TRUE, method="ML")
refit2 <- Arima(x, model=fit2, include.mean=TRUE, method="ML")
refit3 <- Arima(x, model=fit3, include.mean=TRUE, method="ML")
refit4 <- Arima(x, model=fit4, include.mean=TRUE, method="ML")
fc1[i] <- forecast(refit1, h=h)$mean[h]
fc2[i] <- forecast(refit2, h=h)$mean[h]
fc3[i] <- forecast(refit3, h=h)$mean[h]
fc4[i] <- forecast(refit4, h=h)$mean[h]
acc_fc1=rbind(acc_fc1, accuracy(fc1, test)[,1:5])
acc_fc2=rbind(acc_fc2, accuracy(fc2, test)[,1:5])
acc_fc3=rbind(acc_fc3, accuracy(fc3, test)[,1:5])
acc_fc4=rbind(acc_fc4, accuracy(fc4, test)[,1:5])
}
result.fc<-cbind(fc1, fc2, fc3, fc4)
# 5 matrices with accuracy measures
result.acc1<-cbind(acc_fc1[,1], acc_fc2[,1], acc_fc3[,1], acc_fc4[,1])
result.acc2<-cbind(acc_fc1[,2], acc_fc2[,2], acc_fc3[,2], acc_fc4[,2])
result.acc3<-cbind(acc_fc1[,3], acc_fc2[,3], acc_fc3[,3], acc_fc4[,3])
result.acc4<-cbind(acc_fc1[,4], acc_fc2[,4], acc_fc3[,4], acc_fc4[,4])
result.acc5<-cbind(acc_fc1[,5], acc_fc2[,5], acc_fc3[,5], acc_fc4[,5])
# if you want to know which model is the best
t(apply(result.acc1, 1, order))
t(apply(result.acc2, 1, order))
t(apply(result.acc3, 1, order))
t(apply(result.acc4, 1, order))
t(apply(result.acc5, 1, order))页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/40795032
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