Change Point Detection with Optimal Transport and Geometric Discrepancy

TL;DR

This paper introduces a new non-parametric change point detection method using optimal transport and geometric discrepancy, capable of identifying multiple change points without prior distribution assumptions.

Contribution

It proposes a novel retrospective change point detection technique that leverages optimal transport and geometric discrepancy, applicable to both known and unknown number of change points.

Findings

Effective in artificial data scenarios

Applicable to real-world datasets

No parametric assumptions needed

Abstract

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