Panos, A; Kosmidis, I; Dellaportas, P; (2023) Scalable Marked Point Processes for Exchangeable and Non-Exchangeable Event Sequences. In: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics. (pp. pp. 236-252). Proceedings of Machine Learning Research (PMLR): Valencia, Spain.

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Abstract

We adopt the interpretability offered by a parametric, Hawkes-process-inspired conditional probability mass function for the marks and apply variational inference techniques to derive a general and scalable inferential framework for marked point processes. The framework can handle both exchangeable and non-exchangeable event sequences with minimal tuning and without any pre-training. This contrasts with many parametric and non-parametric state-of-the-art methods that typically require pre-training and/or careful tuning, and can only handle exchangeable event sequences. The framework's competitive computational and predictive performance against other state-of-the-art methods are illustrated through real data experiments. Its attractiveness for large-scale applications is demonstrated through a case study involving all events occurring in an English Premier League season.

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