I am trying to make estimates of the predictive power of ARIMA models estimated
by the auto.arima() function.
I am looping through a large number of time seiries and fitting ARIMA models
with the following code.
data1 <- read.csv(file = "case.csv", header = T)
data <- data1
output = c(1:length(data))
for(i in 1:length(data))
{
point_data = unlist(data[i], use.names = FALSE)
x = auto.arima(point_data , max.p = 10, max.q = 10, max.P = 0, max.Q =
0, approximation = TRUE)
}
However, I would like to find a way to test the out of sample predictive power
of these models. I can think of a few ways I MIGHT be able to do this but
nothing clean. I am a recen R user and despite my best efforts (looking on the
mailing list, reading documentation) I cant figure out the best way to do this.
I tried including something like this:
output[i] = cor(model_data, real_data)
but with poor results.
Does anyone have any tricks to calculate the R^2 or an ARIMA model. Sample code
would be apreciated.
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