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I'm trying DTW from mlpy, to check similarity between time series.

Should I normalize the series before processing them with DTW? Or is it somewhat tolerant and I can use the series as they are?

All time series stored in a Pandas Dataframe, each in one column. Size is less than 10k points.

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DTW often uses a distance between symbols, e.g. a Manhattan distance $(d(x, y) = {\displaystyle |x-y|} $). Whether symbols are samples or features, they might require amplitude (or at least) normalization. Should they? I wish I could answer such a question in all cases. However, you can find some hints in:

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I am glad you asked ;-)

In 99% of cases, you must z-normalize.

Want to know why? I wrote a tutorial on this, page 46 http://www.cs.unm.edu/~mueen/DTW.pdf

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  • $\begingroup$ Any easy way to do this z-normalizaiton in Python? $\endgroup$
    – KcFnMi
    Jan 3 '17 at 10:00
  • $\begingroup$ And, by the way, nice presentation! $\endgroup$
    – KcFnMi
    Jan 3 '17 at 10:17
  • $\begingroup$ @KcFnMi z-normalization in Python: docs.scipy.org/doc/scipy-0.14.0/reference/generated/… $\endgroup$ Jan 5 '17 at 8:56
  • $\begingroup$ @eamonn Awesome slides, thanks for sharing your experience, I can't up vote the answer enough $\endgroup$
    – Emer
    Mar 14 '19 at 14:59
  • $\begingroup$ @eamonn What if the data is multimodal? $\endgroup$
    – Alex
    Aug 22 '20 at 22:35

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