# Multiple change-point detection for Poisson point processes

arXiv: https://arxiv.org/abs/2302.09103 · HTML: https://mlsanity.com/paper/2302.09103/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2023.

## Built on by (most cited first, 1 of 1)

- [Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model](https://mlsanity.com/paper/2412.08873/) (2024)

## Abstract (arXiv)

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.