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
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?