Fast multi-resolution segmentation for nonstationary Hawkes process using cumulants

Zhou, F; Li, Z; Fan, X; Wang, Y; Sowmya, A; Chen, F

Fast multi-resolution segmentation for nonstationary Hawkes process using cumulants

Zhou, F Li, Z

Fan, X Wang, Y

Sowmya, A Chen, F

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: International Journal of Data Science and Analytics, 2020, 10, (4), pp. 321-330
Issue Date:: 2020-10-01

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Field	Value	Language
dc.contributor.author	Zhou, F
dc.contributor.author	Li, Z https://orcid.org/0000-0002-0784-157X
dc.contributor.author	Fan, X
dc.contributor.author	Wang, Y https://orcid.org/0000-0002-6815-0879
dc.contributor.author	Sowmya, A
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.date.accessioned	2021-01-06T01:06:34Z
dc.date.available	2021-01-06T01:06:34Z
dc.date.issued	2020-10-01
dc.identifier.citation	International Journal of Data Science and Analytics, 2020, 10, (4), pp. 321-330
dc.identifier.issn	2364-415X
dc.identifier.issn	2364-4168
dc.identifier.uri	http://hdl.handle.net/10453/145104
dc.description.abstract	© 2020, Springer Nature Switzerland AG. The stationarity is assumed in the vanilla Hawkes process, which reduces the model complexity but introduces a strong assumption. In this paper, we propose a fast multi-resolution segmentation algorithm to capture the time-varying characteristics of the nonstationary Hawkes process. The proposed algorithm is based on the first- and second-order cumulants. Except for the computation efficiency, the algorithm can provide a hierarchical view of the segmentation at different resolutions. We extensively investigate the impact of hyperparameters on the performance of this algorithm. To ease the choice of hyperparameter, we propose a refined Gaussian process-based segmentation algorithm, which is proved to be a robust method. The proposed algorithm is applied to a real vehicle collision dataset, and the outcome shows some interesting hierarchical dynamic time-varying characteristics.
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	International Journal of Data Science and Analytics
dc.relation.isbasedon	10.1007/s41060-020-00223-3
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing
dc.title	Fast multi-resolution segmentation for nonstationary Hawkes process using cumulants
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-01-06T01:06:31Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	4

Abstract:

© 2020, Springer Nature Switzerland AG. The stationarity is assumed in the vanilla Hawkes process, which reduces the model complexity but introduces a strong assumption. In this paper, we propose a fast multi-resolution segmentation algorithm to capture the time-varying characteristics of the nonstationary Hawkes process. The proposed algorithm is based on the first- and second-order cumulants. Except for the computation efficiency, the algorithm can provide a hierarchical view of the segmentation at different resolutions. We extensively investigate the impact of hyperparameters on the performance of this algorithm. To ease the choice of hyperparameter, we propose a refined Gaussian process-based segmentation algorithm, which is proved to be a robust method. The proposed algorithm is applied to a real vehicle collision dataset, and the outcome shows some interesting hierarchical dynamic time-varying characteristics.

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http://hdl.handle.net/10453/145104