I have a lot of time series with different lengths. I would like to know what are the best practices to fit them to a Bidirectional LSTM model. The problem is a Binary Classification of Sequence to Sequence. So for every time step, I want to predict the binary class.
Currently, I create a tensor for each data frame with the shape of
(1, None, #Features). Then I fit every tensor separately to the model.
Is it better to combine every data frame to a tensor of the shape
(#Time Series, None, #Features) and fit them all at once? Does this make even a difference?
Or could it be better to go with the sliding window approach and split one time-series into smaller windows?
I can't specify a max-length of the time-series so I think I cannot use
pad-sequence from Keras.