Paper
Multiple change-point detection on the circle via isolation using permutation testing
Authors
Sophia Loizidou, Andreas Anastasiou, Christophe Ley
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.10772v1</id>\n <title>Multiple change-point detection on the circle via isolation using permutation testing</title>\n <updated>2026-03-11T13:49:18Z</updated>\n <link href='https://arxiv.org/abs/2603.10772v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.10772v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='stat.ME'/>\n <published>2026-03-11T13:49:18Z</published>\n <arxiv:comment>22 pages, 7 figures</arxiv:comment>\n <arxiv:primary_category term='stat.ME'/>\n <author>\n <name>Sophia Loizidou</name>\n </author>\n <author>\n <name>Andreas Anastasiou</name>\n </author>\n <author>\n <name>Christophe Ley</name>\n </author>\n </entry>"
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