Benefits from superposed Hawkes processes

Hongteng Xu, Dixin Luo, Xu Chen, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The superposition of temporal point processes has been studied for many years, although the usefulness of such models for practical applications has not be fully developed. We investigate superposed Hawkes process as an important class of such models, with properties studied in the framework of least squares estimation. The superposition of Hawkes processes is demonstrated to be beneficial for tightening the upper bound of excess risk under certain conditions, and we show the feasibility of the benefit in typical situations. The usefulness of superposed Hawkes processes is verified on synthetic data, and its potential to solve the cold-start problem of recommendation systems is demonstrated on real-world data.
Original languageEnglish (US)
Title of host publicationInternational Conference on Artificial Intelligence and Statistics, AISTATS 2018
PublisherPMLR
Pages623-631
Number of pages9
StatePublished - Jan 1 2018
Externally publishedYes

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