# Deciding upon the important regressors for a SARIMA model

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?

• Do I need to add some other information on this question? Questions here are usually answered really quick. So I was wondering if I need to elaborate the question. If so, then what other information do I add to it? – Crystal Snow Oct 24 '19 at 10:52