hello,

I have a question about making n step ahead forecasts in cases where test
and validation sets are availiable. For instance, I would like to make one
step ahead forecasts  on the  WWWusage data so I hold out the last 10
observations as the validation set and fit an ARIMA model on the first 90
observations. I then use a for loop to sequentially add 9 of the holdout
observations to make 1 step ahead forecasts for the last 10 periods (see
example code). In cases where there are relatively few periods I want to
forecast for this seems to work fine, however I am working with a rather
large validation set and I need to make n step ahead forecasts for many
periods and it takes a very long time. Is there a more efficient way to do
this?



vset <- WWWusage[91:100]

pred <-c()
for (i in 0:9)
    { fit <-arima(WWWusage[1:(90+i)],c(3,1,0))
      p<- predict(fit,se.fit=F)
      pred <- c(pred, p)
    }
plot(pred,type="o",col=2)
lines(vset,type="o",col=1)


thanks,

Spencer

	[[alternative HTML version deleted]]

