And often times with NuPIC it turns out that B is best forecasted by its own history alone, without using A at all.
This question, of which input field(s) is(are) optimal for a model to forecast field X, is addressed by swarming as well. It goes through different combinations of hyper parameters (including input field subsets), keeping track of which models do best in terms of %error on the ‘predictedField’.
In your case though since there are just 2 possible input fields including the predictedField, you could skip the swarming and do your own sort of grid search. I would test across ranges of:
- Input field subset (‘B’ or ‘A’ & ‘B’)
- Encoding parameters (for instance different min/max values for scalar encoders)
- Time aggregation levels (for instance sampling changes over 5, 10, 30 and 60-minute spans) – to my knowledge swarming doesn’t check this
If the system(s) generating your predictedField can be expected to have any temporal structure then HTM should certainly be worth trying IMO.