msmna93 - 1 year ago 99
R Question

# Ordering Models in a For Loop

Suppose that I have the following "for" loop in R to generate rolling forecasts with model-refitting from a set of four ARMA models. I built the code based on a previous post on the topic (See this link: http://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)
``````

The following code computes various forecasting accuracy measures (see this link for a description of these measures: 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]
``````

My question is:

How can I tell the loop to rank the four estimated models by the five forecasting accuracy measures as above in five distinct matrices?

My solution, but I do not know if I understood correctly what outcome do you want to get.

``````# 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))
``````
Recommended from our users: Dynamic Network Monitoring from WhatsUp Gold from IPSwitch. Free Download