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    $\begingroup$ I recently had to do an exercise that involved clustering time series. All the literature I could find suggested that KMeans was an inappropriate algorithm for doing so, and that I should rely on Dynamic Time Warping instead. You could try that and see if it's more useful for you? Fair warning; it's a pretty computationally heavy (I.E. slow as hell) algorithm. You can find a python implementation here: github.com/alexminnaar/… $\endgroup$ Commented Jul 29, 2019 at 22:14
  • $\begingroup$ You can also use an autoencoder to do this and it might (actually, it will) be much faster than DTW... PCA might be a solution as well... It all depends on your data, can you provide a plot with time as x-axis and sensor measurements as y-axis? Also, you might be interested in reading this: scikit-learn.org/stable/modules/outlier_detection.html $\endgroup$ Commented Jul 31, 2019 at 16:27