You can use it to get a single similarity / closeness number between two timeseries and then feed that into a clustering algorithm.
For instance look at https://github.com/markdregan/K-Nearest-Neighbors-with-Dynamic-Time-Warping as a first idea: if you expand the distance function d = lambda x,y: abs(x-y) to a multivariate local distance d2 = lambda a,b: np.sqrt(float((a[0]-b[0])**2 + (a[1]-b[1])**2) (or any other n-dim metric) Then you have an algorithm that could cluster the timeseries. It does also work when the timeseries are of equal length… Best Mikkel Brynildsen From: scikit-learn <scikit-learn-bounces+mbrynildsen=grundfos....@python.org> On Behalf Of lampahome Sent: 17. januar 2019 08:45 To: Scikit-learn mailing list <scikit-learn@python.org> Subject: Re: [scikit-learn] Any clustering algo to cluster multiple timing series data? Mikkel Haggren Brynildsen <mbrynild...@grundfos.com<mailto:mbrynild...@grundfos.com>> 於 2019年1月17日 週四 下午3:07寫道： What about dynamic time warping ? I thought DTW is used to different length of two datasets But I only get the same length of two datasets. Maybe it doesn't work?
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