# Choosing regressors for inclusion in regression with ARMA errors

I would like to conduct a forecast based on a time series ARIMA-model with multiple exogenous variables. My time series is monthly unemployment data (in percentage) during several years and my regressors are continuous values of viewership Wikipedia traffic data on several Wikipedia articles. Both, the time series and the regressors, have the same length.

How to choose the right regressors to include in the model? Using auto.arima and forecast functions from the "forecast" package in R, my first attempt was to order the regressors according to the best resulting MAE when using each one individually. So, I start by using only 1 regressor (the best MAE), then I add the second best regressor, etc. Nevertheless, this post suggests to choose regressors according to significance but this post by Rob Hyndman suggests using AIC.

How should I proceed? How do I accept/reject regressors?

• This is quite a frequent question, you could benefit from exploring the existing threads more. Of course, in case of conflicting advice, it is valid to ask for reassurance. – Richard Hardy Jul 28 '16 at 13:05
• Thanks Richard Hardy, Im quite new to arima models and how this R package works with forecasts. I have found several threads. The one with more help is the one referred in my post. I just wanted to find some feedback with regard to my approach. – ruthy_gg Jul 28 '16 at 13:30
• Understood. See also my comment under Stephan's answer. – Richard Hardy Jul 30 '16 at 16:33

• @user844924, you can just look at pure PCA literature (without auto.arima) as you would be applying PCA before supplying the first few principal components as xreg in auto.arima. – Richard Hardy Jul 28 '16 at 14:10