For 루프의 모델 순서

Question

4 개의 ARMA 모델 집합에서 모델 수정을 통해 롤링 예측을 생성하려면 다음과 같은 "for"루프가 있다고 가정합니다. 나는이 주제에 이전 게시물을 기반으로 코드를 내장 (링크 참조 : 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) 

다음 코드는 다양한 예측 정확도 측정을 계산을 (: http://127.0.0.1:15135/library/forecast/html/accuracy.html 이러한 조치에 대한 설명은이 링크를 참조).

accuracy(fc1, test)[,1:5] 
accuracy(fc2, test)[,1:5] 
accuracy(fc3, test)[,1:5] 
accuracy(fc4, test)[,1:5] 

내 질문은 :

어떻게 오 별개의 행렬에 위와 같이 다섯 개 가지 예측의 정확도를 측정하여 네 추정 모델을 평가하기 위해 루프를 알 수 있습니까?

는 당신의 도움을 주셔서 감사합니다.

Answer 1

내 솔루션이지만, 나는 당신이 얻고 자하는 결과를 정확히 이해했는지 모르겠다.

# 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))