Online Learning for Mixture of Multivariate Hawkes Processes

TL;DR

This paper introduces an online learning method for Mixture of Multivariate Hawkes Processes that models both latent network structures and complex interactions among actors, demonstrating effectiveness on synthetic and real data.

Contribution

It presents a novel online learning framework that simultaneously captures latent actor clusters and their interactions in multivariate Hawkes processes.

Findings

Effective modeling of actor networks and interactions

Successful application to medical and financial data

Outperforms existing methods in experiments

Abstract

Online learning of Hawkes processes has received increasing attention in the last couple of years especially for modeling a network of actors. However, these works typically either model the rich interaction between the events or the latent cluster of the actors or the network structure between the actors. We propose to model the latent structure of the network of actors as well as their rich interaction across events for real-world settings of medical and financial applications. Experimental results on both synthetic and real-world data showcase the efficacy of our approach.