How to train the same RNN over multiple series?

Question

I have multiple separate time series and would like to train the same LSTM network on them. How to do in this situation? I can't just concatenate timeseries (along time), because I am afraid network will be confused by jumps at the points of concatenation.

How to overcome?

Answer 1

just re-use

model.fit()

on the fresh datasets using the already trained model, simple as that :) !

(given that you do it in Keras)

Answer 2

If I understand your question correctly, the reason you think cannot concatenate your time series into a one dataset is because of their different length. Depending on your problem, you can handle this issue in multiple manners in preprocessing. But the more common way is to use sequence padding. Preprocessing methods are natively implemented in keras: https://keras.io/preprocessing/sequence/.

Hope that answers your question.

EDIT 1:

The answer of @pcko1 anwser would work but'll force you into using a batch size of 1, which might give you a much higher convergence time.