用分层流模型统一处理时空事件的预测与逆向推断。
Arbitrarily Conditioned Hierarchical Flows for Spatiotemporal Events
- 分层流架构结合混合掩码，灵活支持任意事件条件下的建模。
- 在合成与真实数据集上均超越基线，提升预测与逆推精度。
- 适合需要轨迹恢复、缺失位置重建的任务场景。
时空系统中的事件无处不在，但其复杂分布的建模仍具挑战。现有点过程模型常依赖强结构假设，通常仅限于自回归式逐事件预测，难以支持反向推断、轨迹重建及缺失事件位置恢复等任务。我们提出任意条件分层流（ARCH），一种用于时空事件建模的分层流匹配框架。ARCH具备足够表达能力以捕捉复杂事件分布，同时可高效准确计算条件强度，量化瞬时事件风险。基于历史编码-生成解码架构，引入混合掩码策略，实现对任意观测事件的灵活条件化。该方法在单一框架内统一处理预测、反向推断与部分轨迹恢复。在合成与真实数据集上的实验表明，ARCH在预测与条件推断任务中均持续优于现有基线。
原文摘要 · Abstract (English)
Events in spatiotemporal systems are ubiquitous, yet modeling their complex distributions remains challenging. Existing point process models often rely on strong structural assumptions and are typically limited to autoregressive, event-by-event prediction. As a result, they struggle to support broader inference tasks such as inverse inference, trajectory reconstruction, and recovery of missing event locations. We introduce Arbitrarily Conditioned Hierarchical Flows (ARCH), a hierarchical flow matching framework for spatiotemporal event modeling. ARCH is expressive enough to capture complex event distributions while enabling tractable and accurate computation of conditional intensities, which quantify instantaneous event risk. Built on a history-encoder-generative-decoder architecture, ARCH introduces a hybrid masking strategy for flexible conditioning on arbitrary observed events. This enables a unified treatment of forecasting, inverse inference, and partial trajectory recovery within a single framework. Experiments on synthetic and real-world datasets show that ARCH consistently outperforms existing baselines across both prediction and conditional inference tasks.
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