EventFlow: Forecasting Temporal Point Processes with Flow Matching
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911567992324096 |
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| author | Kerrigan, Gavin Nelson, Kai Smyth, Padhraic |
| author_facet | Kerrigan, Gavin Nelson, Kai Smyth, Padhraic |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_07430 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EventFlow: Forecasting Temporal Point Processes with Flow Matching Kerrigan, Gavin Nelson, Kai Smyth, Padhraic Machine Learning 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. |
| title | EventFlow: Forecasting Temporal Point Processes with Flow Matching |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.07430 |