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.
DTW (Dynamic Time Warping) requires prior normalization?
time series
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:Dynamic Time Warping and normalizationSection 1.2.1 of Searching and Mining Trillions of Time Series Subsequences under Dynamic Time WarpingNormalization for Dynamic Time Warping