Time series clustering on large data I am trying to do K-means clustering on my data which has time series length of 3700 and for (latitude,longitude) points of around 6000 in length. However, timeseries clustering using tslearn package even with joblib capabilities is painstaking slow. I m trying to cluster using dynamic time wrapping method. Can anyone suggest better method to cluster my data.
I have tried all the packages in python without any leads. Please let me know if any implementation of time-series clustering available with dtw metric which works in parallel.

*

*Instead of DTW, use cDTW, with a constraint of about 5%. That will give a ~20 times speed up


*If the data is oversampled (as most data is) you can downsample it before using DTW. Since the time is quadratic in length, if you downsample by say 1 in 3, that would be about nine times faster.


*You can combine ideas '1' and '2' to get about 200 times faster...
PS, DTW is not a metric, it is a measure
https://www.cs.unm.edu/~mueen/DTW.pdf
