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K-spectral centroid: extension and optimizations

In this work, we address the problem of unsupervised classification of large time series datasets. We focus on K-Spectral Centroid (KSC), a k-means-like model, devised for time series clustering. KSC relies on a custom dissimilarity measure between time series, which is invariant to time shifting and Y-scaling. KSC has two downsides: firstly its dissimilarity measure only makes sense for non negative time series. Secondly the KSC algorithm is relatively demanding in terms of computation time. In this paper, we present a natural extension of the KSC dissimilarity measure to time series of arbitrary signs. We show that this new measure is a metric distance. We propose to speed up this extended KSC (EKSC) thanks to four exact optimizations. Finally, we compare EKSC to a similar model, K-Shape, on real world datasets.