Neural Temporal Point Processes: A Review

TL;DR

This review paper consolidates current knowledge on neural temporal point processes, discussing design principles, applications, and future challenges in this rapidly evolving field.

Contribution

It provides a comprehensive overview of neural TPP architectures, principles, and application areas, highlighting recent developments and open research challenges.

Findings

Summarizes key design choices for neural TPPs

Reviews diverse application domains of neural TPPs

Identifies open challenges and future research directions

Abstract

Temporal point processes (TPP) are probabilistic generative models for continuous-time event sequences. Neural TPPs combine the fundamental ideas from point process literature with deep learning approaches, thus enabling construction of flexible and efficient models. The topic of neural TPPs has attracted significant attention in the recent years, leading to the development of numerous new architectures and applications for this class of models. In this review paper we aim to consolidate the existing body of knowledge on neural TPPs. Specifically, we focus on important design choices and general principles for defining neural TPP models. Next, we provide an overview of application areas commonly considered in the literature. We conclude this survey with the list of open challenges and important directions for future work in the field of neural TPPs.