Unified Flow Matching for Long Horizon Event Forecasting

• URL: http://arxiv.org/abs/2508.04843v1
• Date: Wed, 06 Aug 2025 19:42:49 GMT
• Title: Unified Flow Matching for Long Horizon Event Forecasting
• Authors: Xiao Shou,
• Abstract summary: We propose a unified flow matching framework for marked temporal point processes.<n>By learning continuous-time flows for both components, our method generates coherent long horizon event trajectories without sequential decoding.<n>We evaluate our model on six real-world benchmarks and demonstrate significant improvements over autoregressive and diffusion-based baselines in both accuracy and generation efficiency.
• Score: 3.0639815065447036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modeling long horizon marked event sequences is a fundamental challenge in many real-world applications, including healthcare, finance, and user behavior modeling. Existing neural temporal point process models are typically autoregressive, predicting the next event one step at a time, which limits their efficiency and leads to error accumulation in long-range forecasting. In this work, we propose a unified flow matching framework for marked temporal point processes that enables non-autoregressive, joint modeling of inter-event times and event types, via continuous and discrete flow matching. By learning continuous-time flows for both components, our method generates coherent long horizon event trajectories without sequential decoding. We evaluate our model on six real-world benchmarks and demonstrate significant improvements over autoregressive and diffusion-based baselines in both accuracy and generation efficiency.

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