Time Series Forecasting for many examples
[Apologies in advance for any confusing or vague language I may use; I'm not a data scientist, so I don't know the proper terminology.]
Say I have a data set of sales volume over time for a retailer that sells screwdrivers. Their product catalog really runs the gamut: flathead, phillips, torx, long, short, every color you can think of, and on and on. If you wanted to forecast demand, you could create a model for one series at a time for each product (e.g. short, yellow, flathead screwdrivers and then medium length, purple, torx drivers with fat handles, etc), or one could aggregate sales for all phillips head screwdrivers or all the different types of screwdrivers in order to collapse them into one series.
For some reason, though, let's say you wanted to use all the data from every type of screwdrivers individually to train a model. For each date, you would have data points for every type of screwdriver in inventory.
What is the "right way" to represent this in RapidMiner?