Change-point detection aims at discovering behavior changes lying behind time sequences data. In this paper, we investigate the case where the data come from an inhomogenous Poisson process or a marked Poisson process. We present an offline multiple change-point detection methodology based on minimum contrast estimator. In particular we explain how to deal with the continuous nature of the process together with the discrete available observations. Besides, we select the appropriate number of regimes through a cross-validation procedure which is really convenient here due to the nature of the Poisson process. Through experiments on simulated and realworld datasets, we show the interest of the proposed method. The proposed method has been implemented in the \texttt{CptPointProcess} R package.

翻译：变点检测旨在发现时间序列数据背后的行为变化。本文研究数据来源于非齐次泊松过程或标记泊松过程的情形。我们提出一种基于最小对比度估计器的离线多重变点检测方法，重点阐述了如何处理过程的连续性与离散观测数据。此外，利用泊松过程的数据特性，通过交叉验证方法选择合理的状态数量。通过在模拟数据集和真实数据集上的实验，我们展示了所提方法的有效性。该方法已在 \texttt{CptPointProcess} R包中实现。