Make Hawkes Processes Explainable by Decomposing Self-Triggering Kernels
This work addresses the need for explainable temporal event modeling in fields like finance or social networks, representing an incremental improvement with a novel composition scheme.
The paper tackles the problem of making Hawkes processes more interpretable by decomposing self-triggering kernels into base components, introducing the first multiplicative kernel composition methods. It demonstrates that this automatic decomposition procedure outperforms existing methods in predicting discrete events on real-world data, though no specific numerical results are provided.
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.