Hawkes graphs

TL;DR

This paper introduces the Hawkes skeleton and Hawkes graph to summarize multivariate Hawkes processes, providing a graph-theoretic framework for model specification, estimation, and analysis, especially suited for high-dimensional data.

Contribution

It presents a novel graph-based representation of Hawkes processes and a nonparametric estimation method that is flexible and scalable for large multitype event streams.

Findings

Effective nonparametric estimation of Hawkes graphs demonstrated

Method handles high-dimensional, large-scale event data

Graph-based approach improves model specification and interpretation

Abstract

This paper introduces the Hawkes skeleton and the Hawkes graph. These objects summarize the branching structure of a multivariate Hawkes point process in a compact, yet meaningful way. We demonstrate how graph-theoretic vocabulary (`ancestor sets', `parent sets', `connectivity', `walks', `walk weights', ...) is very convenient for the discussion of multivariate Hawkes processes. For example, we reformulate the classic eigenvalue-based subcriticality criterion of multitype branching processes in graph terms. Next to these more terminological contributions, we show how the graph view may be used for the specification and estimation of Hawkes models from large, multitype event streams. Based on earlier work, we give a nonparametric statistical procedure to estimate the Hawkes skeleton and the Hawkes graph from data. We show how the graph estimation may then be used for specifying and fitting parametric Hawkes models. Our estimation method avoids the a priori assumptions on the model from a straighforward MLE-approach and is numerically more flexible than the latter. Our method has two tuning parameters: one controlling numerical complexity, the other one controlling the sparseness of the estimated graph. A simulation study confirms that the presented procedure works as desired. We pay special attention to computational issues in the implementation. This makes our results applicable to high-dimensional event-stream data, such as dozens of event streams and thousands of events per component.