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artificial-intelligencetime-seriespattern-recognitionpybrainlstm

Neural network time series normalization

I am using LSTM as the hidden layer function in a time series prediction network. Is input normalization necessary? If it is, is data = data / sum(data) the correct normalization? Should the output also be normalized with the inputs?

Solution

  • Is input normalization necessary?

    No, but it might make your network converge faster. Use this calculation to scale your values to [0,1]:

    normalization.

    Should the output also be normalized with the inputs?

    No, I can't think of a reason why you would ever want to do that.