02-21-2013 02:06 PM
I've a dataset where each observation has a common set of variables. Each observation also has a time series set of variables, with the length of the series changing from observation to observation.
For instance. The max length of the time series is 36. A member who churned after 6 months would have 6 out of 36 time series data points (the remaining 30 would be missing values). Another member who churned after 22 months would have 22 datapoints out of 36.
Something like this:
obs age gender t1 t2 t3 t4 ... t36
1 23 0 9 8 3 . .
2 54 1 8 8 . . .
3 34 1 5 5 6 4 8
I want to create an ensemble model where a model is fitted to each subgroup of members according to the length of their time series. In order to do so, I need to be able to change the role of the unsed time series variables to rejected.
That can be done manually using an endless series of metadata nodes. But I'd like a more flexible code driven solution. Is that possible?