I manipulate the time series using the different structures of the neural networks in order to make a prediction, and I wonder if there is a way to choose the parameters of the networks intelligently? from the characteristics of the signal, namely (trend, seasonality ...) can we choose these parameters that will make learning better?

  • $\begingroup$ Many folks use grid search. Some crazy folks use bounded Nelder Mead. $\endgroup$ – EngrStudent Aug 14 at 14:14

Indeed you can introduce some "unvariant" features to your LSTM network using Conditional RNN that use these features to create the initial hidden state:

I hope this is what you are looking for.

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