# predicting the future for multi-variate time series

There are tons of approaches to model multi-variate time series. For example VAR and FBProphet, to mention a few. The challenge is, how does one predict the future regressors/independent variables (time series). Do people tend to use univariate time series models or do they create multivariate models for each time series and then iterate over them for each future time step (makes more sense embracing the VAR model?).

• You may also have a time series $$X$$ that drives your focal time series $$Y$$, but not vice versa. An example would be the influence of weather on your retail sales. (Can you tell what I do for a living?) In this case, you can use whatever is appropriate to forecast your driver $$X$$, in this case your favorite meteorological forecast.