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Everything in your understanding is correct, except for a nomenclature thing. I would caution you that H isn’t actually a weight matrix. The words “weights” and “parameters” are pretty interchangeable. In the case of H, it’s not a parameter. It depends on the particular sequence x that you observe. It’s not entirely true that you can’t parallelize the RNN ...


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It turns out this was only co-incidentally to do with the increase in the length of the time series. I was using a generator to iterate the training set. With the increase in the length of the time series, I tweaked the generator to use less RAM and introduced a bug. It turned out that the generator was progressively scrambling the training examples. On ...


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There are two general forecasting techniques I will point you towards. The first is hierarchical forecasting. This is a good method if you data has a structure where you need to forecast individual series, but also aggregated series (for example forecast each individual item in a store, plus each store's sales). Rob Hyndman has published quite a few papers ...


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Could use a Bayesian hierarchical model where each person is a different level.


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So @wprime gave a part of the answer. Indeed, we want to set return_sequences=True because we don't just want the final prediction for each sequence, we want all the predictions along the way as well. By then reshaping the input correctly ([batches, timesteps, features]) we get a very good result. This is minimal working example: import os os.environ['...


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The shape of LSTM inputs needs to be a 3D tensor of dimensions, [batch_size, time_steps, num_features]. Your response variable can either be a separate Numpy array of shape [batch_size, time_steps, 1] or it can be included with the feature; you just need to split it off when passing in training data. It is worth noting, batch size defaults to 32 in Keras/TF. ...


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