Say that I have a univariate time series X(t) that I want to forecast using RNN/LSTM.

I have 2 years of weekly sales data that is seasonal. How many hidden layers and neurons in each layer do I need ? Are there any rules or heuristics for this?

I assume the seasonality and the number of weeks of data would be relevant to the number of hidden layers and neurons?


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