LSTM for time series forcasting 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?
Indeed you can introduce some &quot;unvariant&quot; features to your LSTM network using Conditional RNN that use these features to create the initial hidden state:
https://github.com/philipperemy/cond_rnn
I hope this is what you are looking for.