An explainable machine learning framework for recurrent event data analysis

Qi Lyu and Shaomin Wu

European Journal of Operational Research, 2026, vol. 328, issue 2, 591-606

Abstract: This paper introduces a novel explainable temporal point process (TPP) model, Stratified Hawkes Point Process (SHPP), for modelling recurrent event data (RED). Unlike existing approaches that treat temporal influence as a black box or rely on post-hoc explanations, SHPP structurally decomposes event intensities into semantically meaningful components for describing self-, Markovian, and joint influences. This decomposition enables direct quantification of how past events contribute to future event risks, termed as influence values. We further provide a sufficient condition for mean-square stability based on kernel decay, ensuring long-term boundedness of intensities and realistic behavioural predictions. Experiments and an e-commerce case study demonstrate SHPP’s ability to deliver accurate, interpretable, and stable modelling of complex event-driven systems.

Keywords: (R) explainable machine learning; Counting process; Hawkes process; Stability; Explainable artificial intelligence (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:328:y:2026:i:2:p:591-606

DOI: 10.1016/j.ejor.2025.09.005

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