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:
- Does the above statement mean that I should normalize the entire time series and each subsequence or just each subsequence?
- What's the reasoning for either choice?
Thank you very much for your answer.