Multiple change-point detection for some point processes
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 to 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 \texttt{CptPointProcess}.
Cite
@article{arxiv.2302.09103,
title = {Multiple change-point detection for some point processes},
author = {C. Dion-Blanc and D. Hawat and E. Lebarbier and S. Robin},
journal= {arXiv preprint arXiv:2302.09103},
year = {2024}
}