I have 14 months(01/07/2018 to 30/08/2019) of one minute data, which I have aggregated to 10 mins block. So I have a data of dimension "61056 * 350". From this I am using 12 months of data to train the model and 2 months of data to validate it. I am using R version 3.6.0 to build my model.
Is there a limit for number of regressors, beyond which more regressors should not be used? How can one decide upon the important regressors?
I tried using
summary(tslm(train$DV~as.matrix(xreg))) to choose the important regressors. But I'm using SARIMA to train my model. Is that a correct approach? Else what can I follow?