Cumulative Hazard Function Based Efficient Multivariate Temporal Point Process Learning

arXiv:2404.13663v1 Announce Type: new
Abstract: Most existing temporal point process models are characterized by conditional intensity function. These models often require numerical approximation methods for likelihood evaluation, which potentially hurts their performance. By directly modelling the integral of the intensity function, i.e., the cumulative hazard function (CHF), the likelihood can be evaluated accurately, making it a promising approach. However, existing CHF-based methods are not well-defined, i.e., the mathematical constraints of CHF are not completely satisfied, leading to untrustworthy results. For …

arxiv cs.ai cs.lg function multivariate process stat.ml temporal type