I have been working with the forecast package in R a lot, recently. And my question might seem trivial (or not, maybe I'm missing something), but for the life of me I can't seem to find a way to fit an Arima model with exogenous variables (
xreg argument) that has been computed by the
auto.arima function to previously unseen test data.
So, I'm basically trying to do the following:
library(forecast) fit <- auto.arima(trainingdata, xreg = trainingvariables)
...and then I would like to "apply" the model to new test data, for which I also have new exogenous variables available. I can see the following methods:
That returns one-step in-sample forecasts, so, in effect, that's exactly what I want. Except that it's in-sample. However, I would like to calculate one-step out-of-sample forecasts (with new exogenous variables that I have available). Another method:
forecast(fit, xreg = newvariables, h = ...)
That works for exactly one step, but then seems to merely forecast the trainingdata stored in the model fit. But I don't think I can use new testdata here? (So, I can't use this method for testing one-step prediction accuracy.) One more idea:
fit2 <- Arima(testdata, model = fit)
According to the manual, if the
model parameter is used, "this same model is
fitted to [testdata] without re-estimating any parameters". Great, but I don't think I can supply any new exogenous variables, can I?
I really think, I must be missing something simple. Any help would be much appreciated.