提出统一流匹配框架，实现事件序列的非自回归联合建模。
Unified Flow Matching for Long Horizon Event Forecasting
- 通过连续与离散流匹配，联合建模事件间隔与类型
- 在六大数据集上显著提升长程预测准确率与生成效率
- 适合需要高效长序列生成的医疗、金融等场景
建模长时序带标记事件序列是医疗、金融和用户行为分析中的基础挑战。现有神经时间点过程模型多为自回归式，逐步预测下一个事件，限制了效率并导致误差累积。本文提出一种针对带标记时间点过程的统一流匹配框架，通过连续与离散流匹配，实现非自回归的联合建模，同时学习事件间隔与事件类型的连续时间流，无需逐步解码即可生成一致的长时序事件轨迹。在六个真实世界基准数据集上的实验表明，该方法在准确率与生成效率方面均显著优于自回归及基于扩散的基线模型。
原文摘要 · Abstract (English)
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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