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We present novel retrospective change point detection approach based onoptimal transport and geometric discrepancy. The method does not require anyparametric assumptions about distributions separated by change points. It canbe used both for single and multiple change point detection and estimation,while the number of change points is either known or unknown. This result isachieved by construction of a certain sliding window statistic from whichchange points can be derived with elementary convex geometry in a specificHilbert space. The work is illustrated with computational examples, bothartificially constructed and based on actual data.

Change Point Detection with Optimal Transport and Geometric Discrepancy