Hawkes Processes Modeling, Inference and Control: An Overview

TL;DR

This paper provides a comprehensive overview of recent advances in modeling, inference, and control of Hawkes Processes, highlighting their applications across various domains and recent algorithmic developments.

Contribution

It offers a broad survey of recent tools, algorithms, and methods for Hawkes Processes, serving as an accessible introduction for newcomers.

Findings

Recent algorithms have improved inference efficiency.

Hawkes Processes are widely applied in finance, social networks, and natural events.

New tools are emerging for modeling and controlling self-exciting processes.

Abstract

Hawkes Processes are a type of point process which models self-excitement among time events. It has been used in a myriad of applications, ranging from finance and earthquakes to crime rates and social network activity analysis.Recently, a surge of different tools and algorithms have showed their way up to top-tier Machine Learning conferences. This work aims to give a broad view of the recent advances on the Hawkes Processes modeling and inference to a newcomer to the field.