EventFlow: Forecasting Continuous-Time Event Data with Flow Matching

• URL: http://arxiv.org/abs/2410.07430v1
• Date: Wed, 9 Oct 2024 20:57:00 GMT
• Title: EventFlow: Forecasting Continuous-Time Event Data with Flow Matching
• Authors: Gavin Kerrigan, Kai Nelson, Padhraic Smyth,
• Abstract summary: We propose EventFlow, a non-autoregressive generative model for temporal point processes. Our model builds on the flow matching framework in order to directly learn joint distributions over event times, side-stepping the autoregressive process.
• Score: 12.976042923229466
• License: http://creativecommons.org/licenses/by/4.0/
• 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 be unsatisfactory in forecasting longer horizons due to cascading errors. We propose EventFlow, a non-autoregressive generative model for temporal point processes. Our model builds on the flow matching framework in order to directly learn joint distributions over event times, side-stepping the autoregressive process. EventFlow is likelihood-free, easy to implement and sample from, and either matches or surpasses the performance of state-of-the-art models in both unconditional and conditional generation tasks on a set of standard benchmarks

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