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Multiple change-point detection for Poisson point processes
[Submitted on 17 Feb 2023 (v1), last revised 9 Jun 2026 (this ve · 2026-06-10 · via stat updates on arXiv.org

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Abstract:The aim of change-point detection is to identify behavioral shifts within time series data. This article focuses on scenarios where the data is derived from an inhomogeneous Poisson process or a marked Poisson process. We present a methodology for detecting multiple offline change-points using a minimum contrast estimator. Specifically, we address how to manage the continuous nature of the process given the available discrete observations. Additionally, we select the appropriate number of changes via a cross-validation procedure which is particularly effective given the characteristics of the Poisson process. Lastly, we show how to use this methodology for self-exciting processes with changes in the intensity. Through experiments, with both simulated and real datasets, we showcase the advantages of the proposed method, which has been implemented in the R package.

Submission history

From: Stephane Robin [view email]
[v1] Fri, 17 Feb 2023 19:19:57 UTC (1,616 KB)
[v2] Wed, 10 Jan 2024 08:10:41 UTC (799 KB)
[v3] Wed, 6 Nov 2024 13:24:19 UTC (866 KB)
[v4] Tue, 9 Jun 2026 08:28:15 UTC (894 KB)