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arXiv · 2302.09103

Multiple change-point detection for Poisson point processes

C. Dion-Blanc·D. Hawat·E. Lebarbier·S. Robin
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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.

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C. Dion-Blanc, D. Hawat, E. Lebarbier, S. Robin. 2023-02-17. Multiple change-point detection for Poisson point processes. https://arxiv.org/abs/2302.09103

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