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arXiv · 2105.14574

Scalable Marked Point Processes for Exchangeable and Non-Exchangeable Event Sequences

Aristeidis Panos·Ioannis Kosmidis·Petros Dellaportas
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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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Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas. 2021-05-30. Scalable Marked Point Processes for Exchangeable and Non-Exchangeable Event Sequences. https://arxiv.org/abs/2105.14574

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