Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches

TL;DR

This survey reviews recent advances in temporal point processes, focusing on Bayesian, neural, and large language model approaches, highlighting their developments, applications, and future challenges.

Contribution

It provides a comprehensive overview of recent TPP research across Bayesian, deep learning, and LLM frameworks, integrating fundamental concepts and practical applications.

Findings

Neural TPPs offer enhanced flexibility over traditional models.

Large language models provide new avenues for event sequence analysis.

The survey identifies key challenges and future research directions.

Abstract

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.