Multiple change-point detection on the circle via isolation using permutation testing

TL;DR

This paper introduces PCID, a permutation-based method for detecting multiple change-points in circular signals, demonstrating robustness across various noise types and real-world applications.

Contribution

The paper presents a novel permutation-based approach for multiple change-point detection in circular data, including a new contrast function and validation on real datasets.

Findings

Effective detection in various noise conditions

Robust performance with serially correlated noise

Successful application to real-world datasets

Abstract

In this paper we propose a new method for multiple change-point detection for piecewise-constant circular signals, a setting that, despite its importance in many scientific domains, remains comparatively under-explored. The proposed method, Permutation-based Circular Isolate-Detect, denoted PCID, uses an appropriately chosen contrast function and permutation testing to detect change-points in an offline manner, for the data sequence under consideration. Prior to detection, PCID isolates the change-points. The contrast function used is derived under the assumption of von Mises distribution for the noise, but we show that the method is robust and performs well for other distributions as well. Simulations are used to showcase the usability of the method in different signal and noise structures, including serially correlated noise. In order to exhibit the practical relevance of the method in real-world applications, PCID is applied to three real-world datasets, namely flare, acrophase and wave data.