2021/05/08 by Rafael Lima, Lima, Rafael
Biochemistry, Genetics and Molecular Biology · Mathematics · #Diffusion and Search Dynamics #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Point processes and geometric inequalities
paper · pdf · doi:10.48550/arxiv.2105.03800
openalex publication_date 2021/05/08 · openalex created_date 2021/05/24 · openalex updated_date 2026/07/28
Hawkes Processes have undergone increasing popularity as default tools for modeling self- and mutually exciting interactions of discrete events in continuous-time event streams. A Maximum Likelihood Estimation (MLE) unconstrained optimization procedure over parametrically assumed forms of the triggering kernels of the corresponding intensity function are a widespread cost-effective modeling strategy, particularly suitable for data with few and/or short sequences. However, the MLE optimization lacks guarantees, except for strong assumptions on the parameters of the triggering kernels, and may lead to instability of the resulting parameters .In the present work, we show how a simple stabilization procedure improves the performance of the MLE optimization without these overly restrictive assumptions.This stabilized version of the MLE is shown to outperform traditional methods over sequences of several different lengths.