Work

Multiple change-point detection for Poisson processes

Year: 2026

Type: article

Abstract: The aim of change-point detection is to identify behavioral shifts within time series data. This article focuses on scenarios where the data are derived from a heterogeneous 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.

Source: Computational Statistics & Data Analysis

DOI: 10.1016/j.csda.2026.108464

Authors: Charlotte Dion-Blanc, Diala Hawat, Émilie Lebarbier, Stéphane Robin