提出S2P2模型，用连续时间状态空间建模事件序列，提升预测准确率。
Deep Continuous-Time State-Space Models for Marked Event Sequences
- 用随机跳跃微分方程与非线性结构构建连续时间点过程模型
- 在8个真实数据集上平均性能比现有方法高33%
- 适合需要捕捉复杂时间依赖的医疗、金融等场景
标记时间点过程（MTPPs）用于建模不规则时间间隔发生的事件序列，在医疗、金融和社交网络等领域有广泛应用。我们提出状态空间点过程（S2P2）模型，一种新颖且高效的模型，借鉴现代深度状态空间模型技术，克服了现有MTPP模型的局限性，同时引入连续时间事件序列的强归纳偏置，这是离散序列模型（如RNN、Transformer）所不具备的。受经典线性霍克斯过程启发，我们设计了一种将随机跳跃微分方程与非线性操作交替的架构，构建出基于强度的高表达力MTPP模型，无需对强度施加限制性参数假设。该方法实现高效训练与推理，支持并行扫描，达到线性复杂度与次线性扩展性，同时保持高表达力。实证表明，S2P2在八个真实数据集上均取得最先进的预测似然性能，平均优于最佳现有方法33%。
原文摘要 · Abstract (English)
Marked temporal point processes (MTPPs) model sequences of events occurring at irregular time intervals, with wide-ranging applications in fields such as healthcare, finance and social networks. We propose the state-space point process (S2P2) model, a novel and performant model that leverages techniques derived for modern deep state-space models (SSMs) to overcome limitations of existing MTPP models, while simultaneously imbuing strong inductive biases for continuous-time event sequences that other discrete sequence models (i.e., RNNs, transformers) do not capture. Inspired by the classical linear Hawkes processes, we propose an architecture that interleaves stochastic jump differential equations with nonlinearities to create a highly expressive intensity-based MTPP model, without the need for restrictive parametric assumptions for the intensity. Our approach enables efficient training and inference with a parallel scan, bringing linear complexity and sublinear scaling while retaining expressivity to MTPPs. Empirically, S2P2 achieves state-of-the-art predictive likelihoods across eight real-world datasets, delivering an average improvement of 33% over the best existing approaches.
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