I'm using Dynamic time warping (DWT) as a distance measure for comparing two multivariate time-series. I want to be able to cluster data using DTW as distance measure, since time-series may be shifted, skewed.
Since there are a couple of parameters I should normalize the series so that all the parameters have the same influence when trying to determine whether time-series are similar. I'm using Euclidean distance as local distance for DTW.
My question is - how to determine whether I should use normalization (subtract min and divide by max) or standardization (subtract mean and divide by standard deviation)?
Moreover, can anyone explain to me what is the point with standardization? I understand that it can help me determine how many standard deviations are values far from their mean, but why would that improve my similarity measure when comparing two time-series?
I'm not a statistician, so any explanation would be great. I understand that normalization would give me values in range [0,1] so that all parameters have values in same range, but what will I get by standardization?
Finally, should I divide each time-series by the standard deviation of the whole dataset, or only by standard deviation of the time-series I'm standardizing?
I must also emphasize that my data does not belong to a normal distribution.