I am trying to use LSTM in Keras and I am not sure whether I should used statefull or stateless LSTM. I have read many resources online but seem like they do not apply to my case.
I have a long predictor series
X=[X1,X2,....,Xn] and a long response series
y=[0,0,...,1,1,0,...0]. They have the same length and the response can only take value 1 or 0. My plan is to subsample the long predictor series and use the short series (length 4) to predict the response for the next 3 time points. So my training data look look this
[X1,X2,X3,X4],[y5,y6,y7] [X2,X3,X4,X5],[y6,y7,y8] ...
If I use all these short series (samples) available, I think I should choose stateful. However, because there are a lot more 0 in
y compared to 1, I will keep all the samples that has 1 in the short response series (ex: keep this sample
[y5=0,y6=1,y7=0]) but I will randomly drop a lot of other samples just to make the data balance.
I am not sure whether I should use stateful here, since some short series may be very far away from each other.