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arXiv · 2410.07430

EventFlow: Forecasting Temporal Point Processes with Flow Matching

Gavin Kerrigan·Kai Nelson·Padhraic Smyth
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Abstract

Continuous-time event sequences, in which events occur at irregular intervals, are ubiquitous across a wide range of industrial and scientific domains. The contemporary modeling paradigm is to treat such data as realizations of a temporal point process, and in machine learning it is common to model temporal point processes in an autoregressive fashion using a neural network. While autoregressive models are successful in predicting the time of a single subsequent event, their performance can degrade when forecasting longer horizons due to cascading errors and myopic predictions. We propose EventFlow, a non-autoregressive generative model for temporal point processes. The model builds on the flow matching framework in order to directly learn joint distributions over event times, side-stepping the autoregressive process. EventFlow is simple to implement and achieves a 20%-53% lower forecast error than the nearest baseline on standard TPP benchmarks while simultaneously using fewer model calls at sampling time.

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Gavin Kerrigan, Kai Nelson, Padhraic Smyth. 2024-10-09. EventFlow: Forecasting Temporal Point Processes with Flow Matching. https://arxiv.org/abs/2410.07430

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