Multivariate prediction model using R Imagine having a large database coming from a super market chain with panel data, whose cross-section variables are the different products sold in each super-market. The frequency is daily, that is you have one value for each product sold in each super-market per day. 
Beyond their own history, you have the time-series of other variables e.g. dummy variables that signal the existence of offers.

Your objective is to predict the number of goods sold for each product in each super-market.

Which functions would you use in R to make a week-ahead multivariate forecast? 
Which approach would you suggest?
You could try also  R  h2o

You  have implementations  for   random forest,   gbm , linear models and also   deep  learning .
THe  advantage is  that    h2o is pretty   fast  compared  to   other  implementations