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Here I found this very nice presentation. On page 46 one can read the following:

Essentially all datasets must have every subsequence z-normalized. There are a handful of occasions where it does not make sense to z-normalize, but in those cases, DTW probably does not make sense either.

I tried to use Z-Normalization with the following code in Python:

trainX = stats.zscore(trainX)
testX = stats.zscore(testX)

I.e. I normalized the entire data-set. This resulted in a slighlty worse performance of my classifier. On page 48 of the presentation mentioned above I read the following:

Preempting a common misunderstanding: It is not sufficient to normalize the entire time series. You must normalize each subsequence.

Concerning this I have the following questions:

  1. Does the above statement mean that I should normalize the entire time series and each subsequence or just each subsequence?
  2. What's the reasoning for either choice?

Thank you very much for your answer.

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You should JUST normalize each sequence.

BTW, 128 test datasets here https://www.cs.ucr.edu/~eamonn/time_series_data_2018/

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  • $\begingroup$ Thank you very much for your reply. I'll give it a try. $\endgroup$ – Hagbard Nov 8 '18 at 8:29

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