Neural Networks for Parameter Estimation of the Discretely Observed Hawkes Process

TL;DR

This paper introduces a neural network-based likelihood-free method for estimating parameters of discretely observed Hawkes processes, offering comparable accuracy to existing methods but with reduced computational time.

Contribution

It presents a novel neural network approach using simulated data and a simple summary statistic for efficient parameter estimation of Hawkes processes from count data.

Findings

Comparable mean-squared error to existing estimators

Significantly faster computational time

Effective bias correction and variance estimation

Abstract

When the sample path of a Hawkes process is observed discretely, such that only the total event counts in disjoint time intervals are known, the likelihood function becomes intractable. To overcome the challenge of likelihood-based inference in this setting, we propose to use a likelihood-free approach to parameter estimation, where simulated data is used to train a fully connected neural network (NN) to estimate the parameters of the Hawkes process from a summary statistic of the count data. A naive imputation estimate of the parameters forms the basis of our summary statistic, which is fast to generate and requires minimal expert knowledge to design. The resulting NN estimator is comparable to the best extant approximate likelihood estimators in terms of mean-squared error but requires significantly less computational time. We also propose to use a bootstrap procedure for bias correction and variance estimation. The proposed estimation procedure is applied to weekly count data for two infectious diseases, with a time-varying background rate used to capture seasonal fluctuations in infection risk.