综述神经时空点过程的研究进展与挑战，助力复杂事件建模。
Neural Spatiotemporal Point Processes: Trends and Challenges
- 将深度学习融入时空点过程，捕捉事件的复杂依赖关系
- 系统梳理方法分类与核心设计，揭示当前研究共性与差异
- 适合从事时空数据分析、事件建模的科研人员参考
时空点过程（STPPs）是用于连续空间和时间中事件发生的概率模型。现实世界事件数据通常表现出复杂的依赖关系和异质动态特性。通过引入现代深度学习技术，神经时空点过程能够比传统方法更有效地建模这些复杂性。因此，神经方法与时空点过程的融合已成为一个活跃且快速发展的研究领域。本文对现有方法进行分类，统一关键设计选择，并解释该数据模态面临的挑战。进一步探讨新兴趋势与多样化应用领域。最后，识别文献中的开放问题与研究空白。
原文摘要 · Abstract (English)
Spatiotemporal point processes (STPPs) are probabilistic models for events occurring in continuous space and time. Real-world event data often exhibit intricate dependencies and heterogeneous dynamics. By incorporating modern deep learning techniques, STPPs can model these complexities more effectively than traditional approaches. Consequently, the fusion of neural methods with STPPs has become an active and rapidly evolving research area. In this review, we categorize existing approaches, unify key design choices, and explain the challenges of working with this data modality. We further highlight emerging trends and diverse application domains. Finally, we identify open challenges and gaps in the literature.
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