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For training a Hidden Markov Model (HMM) on a multivariate, continuous time series, is it preferable to scale the data somehow? Some pre-processing steps may be:

  1. Normalize to 0-mean and unit-variance
  2. Scale to [-1, 1] interval
  3. Scale to [0, 1] interval

With neural networks, the rationale behind scaling is to get an "un-squished" error surface that is easier to navigate in.

HMMs use the Baum-Welch algorithm, which is a variation on the Expectation Maximization (EM) algorithm, to learn parameters.

Is EM sensitive to scale of features? Is there some motivation for normalization for HMMs?

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