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Would it make sense for stateful LSTM (or LSTM in general) if in one epoch I feed [0-9],[10-19],[20-29],[30-39]...[990-999] (with corresponding labels/Y data) from my dataset. When I've presented all of the data for that epoch I then call model.reset_states()

After that epoch, could I then move the window forward by some arbitrary amount e.g. 2, so I then feed in [2-11],[12-21],[22-31],[32-41]...[982-991]

I would do that for 99 batches (the final sequence in the batch is now length 8 so I can't make a complete sequence).

Would doing that make sense? That way the network learns sequences from different starting/end points, and has differing output/Y values to train against.

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I don’t see anything wrong with that. In fact it sounds like a good form of data augmentation.

It does sound like you are training with a batch size of 1, which may be slow. You could think about creating batches in a similar way, where the first elements of the batches are the sequences 1-10, 11-20, ..., the second elements of the batches are 2-12, 13-23, ..., all the way to 9-19, 10-29, ...

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  • $\begingroup$ Thanks kbrose. I've implemented this method and seems to work well. Yes at the time of writing I was using batch size of 1 because I have multi-series data, which was of varying lengths, so I was using bs=1 to enable all data to be used. But now I just discard a series if I can't make up a bigger batch size. I figure if the series was so small then a stateful LSTM wouldn't be able to build much state anyway for it to be useful. It may even be detrimental to the learning. $\endgroup$ – BigBadMe Aug 3 '18 at 19:13
  • $\begingroup$ Could I ask a favour; I'm new to LSTM and have a few unanswered questions on the topic. Would you mind looking to see if there's any other questions you might know the answer to: datascience.stackexchange.com/users/51125/… $\endgroup$ – BigBadMe Aug 3 '18 at 19:14

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