Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe

TL;DR

This paper introduces a Frank-Wolfe based method for efficiently learning multidimensional Hawkes processes, achieving comparable or better accuracy with significantly faster runtime across various applications.

Contribution

It adapts the Frank-Wolfe algorithm for faster and accurate parameter estimation in multidimensional Hawkes processes, improving over existing first-order methods.

Findings

Faster runtime compared to existing methods

Maintains or improves accuracy in parameter estimation

Effective across multiple application domains

Abstract

Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data. Multidimensional Hawkes processes model both the self and cross-excitation between different types of events and have been applied successfully in various domain such as finance, epidemiology and personalized recommendations, among others. In this work we present an adaptation of the Frank-Wolfe algorithm for learning multidimensional Hawkes processes. Experimental results show that our approach has better or on par accuracy in terms of parameter estimation than other first order methods, while enjoying a significantly faster runtime.