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.


You should JUST normalize each sequence.

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

  • $\begingroup$ 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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