综述时间点过程的贝叶斯、神经网络与大模型方法，揭示其建模事件序列的新范式。
Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches
- 从贝叶斯、神经网络到大模型，系统梳理三类时间点过程建模方法
- 覆盖模型设计、参数估计及经典应用领域，涵盖多个实际场景
- 适合关注时序建模前沿的研究者和工业应用开发者
时间点过程（Temporal Point Processes, TPPs）是用于描述连续时间中事件序列的随机过程模型。传统统计型TPPs历史悠久，已有众多模型被成功应用于多个领域。近年来，深度学习的发展推动了神经网络型TPPs的兴起，显著提升了对复杂时序动态的建模能力。大语言模型（LLMs）的出现进一步激发研究热情，通过其强大的上下文理解能力为事件序列建模与分析提供了新可能。本文综述了从贝叶斯、深度学习到大模型三方面的最新研究进展。首先介绍TPPs的基础概念，随后深入讨论三类框架下的模型设计与参数估计技术。同时回顾经典应用领域以凸显其实际价值。最后，指出当前挑战并展望未来研究方向。
原文摘要 · Abstract (English)
Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time. Traditional statistical TPPs have a long-standing history, with numerous models proposed and successfully applied across diverse domains. In recent years, advances in deep learning have spurred the development of neural TPPs, enabling greater flexibility and expressiveness in capturing complex temporal dynamics. The emergence of large language models (LLMs) has further sparked excitement, offering new possibilities for modeling and analyzing event sequences by leveraging their rich contextual understanding. This survey presents a comprehensive review of recent research on TPPs from three perspectives: Bayesian, deep learning, and LLM approaches. We begin with a review of the fundamental concepts of TPPs, followed by an in-depth discussion of model design and parameter estimation techniques in these three frameworks. We also revisit classic application areas of TPPs to highlight their practical relevance. Finally, we outline challenges and promising directions for future research.
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