I have a few questions regarding the topic and I hope someone might have experience with any of them.
What I am trying to do is train an LSTM network, whose input is a sequence of N steps in a XYZ space (i.e 3 features over N point per sample, each point is part of some coordinate in space) and i want to predict the next point in the same XYZ space.
Note: Not all samples are from the same DB, meaning the XYZ space varies between some samples/
My questions are:
- Say I want to use min-max scaling, do I scale across ALL samples at once or scale per XYZ space? say i have 100 samples from XYZ_A and 50 from XYZ_B, do i take my min/max from 150 point of both space or not?
- should I also scale my output labels? and if so should I use them in the initial scale or should I only scale on my train inputs and use that scaler on my outputs?
- When I want to make a prediction after training, should I scale the data according to my training data?
OR since my features are relatively from the same domain should I skip scaling all together?