Benefits from Superposed Hawkes Processes

Hongteng Xu, Dixin Luo, Xu Chen, Lawrence Carin
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR 84:623-631, 2018.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v84-xu18c, title = {Benefits from Superposed Hawkes Processes}, author = {Xu, Hongteng and Luo, Dixin and Chen, Xu and Carin, Lawrence}, booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics}, pages = {623--631}, year = {2018}, editor = {Storkey, Amos and Perez-Cruz, Fernando}, volume = {84}, series = {Proceedings of Machine Learning Research}, month = {09--11 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v84/xu18c/xu18c.pdf}, url = {https://proceedings.mlr.press/v84/xu18c.html}, 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.} }
Endnote
%0 Conference Paper %T Benefits from Superposed Hawkes Processes %A Hongteng Xu %A Dixin Luo %A Xu Chen %A Lawrence Carin %B Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2018 %E Amos Storkey %E Fernando Perez-Cruz %F pmlr-v84-xu18c %I PMLR %P 623--631 %U https://proceedings.mlr.press/v84/xu18c.html %V 84 %X 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.
APA
Xu, H., Luo, D., Chen, X. & Carin, L.. (2018). Benefits from Superposed Hawkes Processes. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 84:623-631 Available from https://proceedings.mlr.press/v84/xu18c.html.

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