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.

翻译：时间点过程（TPPs）是用于刻画连续时间上事件序列的随机过程模型。传统统计TPPs拥有悠久的历史，研究者提出了众多模型，并在不同领域成功应用。近年来，深度学习的进步推动了神经网络TPPs的发展，使其在捕捉复杂时间动态方面展现出更强的灵活性和表达能力。大语言模型（LLMs）的出现进一步激发了研究热情，通过利用其丰富的语境理解能力，为事件序列的建模与分析提供了新可能。本综述从贝叶斯方法、深度学习方法和大语言模型方法三个视角，全面梳理了近期TPPs的研究成果。我们首先回顾TPPs的基本概念，随后深入探讨这三种框架下的模型设计与参数估计技术。同时，我们重新审视了TPPs的经典应用领域以强调其实际价值。最后，我们指出了当前面临的挑战及未来有前景的研究方向。