# Multivariate ARIMA modelling in R

I am currently using the Marima package for R invented by Henrik Spliid in order to forecast multivariate time series with ARIMA. Overview can be found here:

When using the Marima function, it is required to define both the order of AR(p) and MA(q) first.

My question is, how can I determine appropriate values for p and q?

I know when it comes to univariate ARIMA analysis, that auto.arima gives a good suggestion for p and q. However, when I use auto.arima for every single univariate time series I want to analyze, there are (slightly) different suggestions for each time series. (For example (2,2,1) for the first, (1,1,1) for the second and so on)

Since I want to analyze all of the time series combined in the multivariate ARIMA model and I only can choose one value for each p and q (if I understood it correctly), I wonder how I can choose those values the most accurate way.

Could I just try to run the model a couple times and see what values for p and q work best (e.g. by testing the residuals of the forecast)?

• R package bigtime could be used as an alternative to marima or to give some hints for your problem of lag order selection. I suspect bigtime might be superior for most purposes (forecasting, variable selection) because it is based on penalized estimation methods, though I have no personal experience with any of the packages. It would be interesting to find out which one works best for your data. – Richard Hardy Nov 13 '18 at 17:02