# Make Hawkes Processes Explainable by Decomposing Self-Triggering Kernels

## TL;DR

This paper introduces a novel framework for decomposing Hawkes Processes into interpretable kernels, enhancing understanding of event dependencies and improving prediction accuracy in real-world datasets.

## Contribution

It presents the first multiplicative kernel composition method for Hawkes Processes, enabling automatic decomposition and better interpretability of event triggering mechanisms.

## Key findings

- Outperforms existing methods in event prediction accuracy
- Provides interpretable decomposition of self-triggering kernels
- First to introduce multiplicative kernel composition for Hawkes Processes

## Abstract

Hawkes Processes capture self-excitation and mutual-excitation between events when the arrival of an event makes future events more likely to happen. Identification of such temporal covariance can reveal the underlying structure to better predict future events. In this paper, we present a new framework to decompose discrete events with a composition of multiple self-triggering kernels. The composition scheme allows us to decompose empirical covariance densities into the sum or the product of base kernels which are easily interpretable. Here, we present the first multiplicative kernel composition methods for Hawkes Processes. We demonstrate that the new automatic kernel decomposition procedure outperforms the existing methods on the prediction of discrete events in real-world data.