you can have a look at : https://tslearn.readthedocs.io/en/latest/
Alex On Thu, Jan 17, 2019 at 9:01 AM Mikkel Haggren Brynildsen <mbrynild...@grundfos.com> wrote: > > 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> 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? > > > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
