EventFlow: Forecasting Continuous-Time Event Data with Flow Matching
<|reference_start|>EventFlow: Forecasting Continuous-Time Event Data with Flow Matching: 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<|reference_end|>
@article{kerrigan2024eventflow:,
    title={EventFlow: Forecasting Continuous-Time Event Data with Flow Matching},
    author={Gavin Kerrigan, Kai Nelson, Padhraic Smyth},
    journal={arXiv preprint arXiv:2410.07430},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07430},
    primaryClass={cs.LG stat.ML}
}