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[D] End-to-end normalization for deep learning of time series?

Has anyone had any experience with this? I have a rather wide feature set in my time series data, many features with vastly different scales. I’m attempting to batch-train an LSTM autoencoder. So far, I have come to the following conclusions:

  • I’m reluctant to use first differences as I don’t want to destroy level information.
  • I don’t want to z-score normalize data before training as many components are non-stationary
  • I don’t want to use sliding min-max or sliding z-score as that destroys any volatility information between subsequent minibatches

So far, the following thoughts have come to mind:

  • Using layer normalization, yielding equal normalization statistics for all features accross my minibatch. Destroys however information between individual features in a given sample
  • Manual z-score operations inside my network. Take the resulting statistics, pass through linear layer to adapt dimensionality and initialize the LSTM hidden layer. So far, doesn’t really work. But a variant of this (perhaps concatenating to the lstm output…?) seems to be my current focus.
  • This approach seems promising:
  • … but somehow, it doesn’t yield good results either. Seems very learning rate dependent which is a bit of a bummer.

Any thoughts or successful ideas?

submitted by /u/brokenAlgorithm
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.