Hi everyone! I am working with multiple time series data and in summary I have to adjust each time series (like inserting average values in data gaps) and then training regression models with mllib for each time series. The adjustment step I did with the adjustement function being mapped for each element of RDD (in this case being the ID[as key] and the grouped by key features). But for the regression models, it was not possible because the functions need RDDs and my solution would be map each element (grouped as time series) to a function of training. How can I deal with time series data in this context with Spark? I did'nt find a way.
Thank you -- Caio
