TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes

Meng, Z; Li, B; Fan, X; Li, Z; Wang, Y; Chen, F; Zhou, F

TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes

Meng, Z Li, B Fan, X Li, Z

Wang, Y Chen, F

Zhou, F

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Publisher:: IOS Press
Publication Type:: Conference Proceeding
Citation:: Frontiers in Artificial Intelligence and Applications, 2024, 392, pp. 810-817
Issue Date:: 2024-10-16

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Field	Value	Language
dc.contributor.author	Meng, Z
dc.contributor.author	Li, B
dc.contributor.author	Fan, X
dc.contributor.author	Li, Z https://orcid.org/0000-0002-0784-157X
dc.contributor.author	Wang, Y
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.contributor.author	Zhou, F
dc.date.accessioned	2025-02-06T04:20:27Z
dc.date.available	2025-02-06T04:20:27Z
dc.date.issued	2024-10-16
dc.identifier.citation	Frontiers in Artificial Intelligence and Applications, 2024, 392, pp. 810-817
dc.identifier.issn	0922-6389
dc.identifier.issn	1879-8314
dc.identifier.uri	http://hdl.handle.net/10453/184998
dc.description.abstract	The classical temporal point process (TPP) constructs an intensity function by taking the occurrence times into account. Nevertheless, occurrence time may not be the only relevant factor, other contextual data, termed covariates, may also impact the event evolution. Incorporating such covariates into the model is beneficial, while distinguishing their relevance to the event dynamics is of great practical significance. In this work, we propose a Transformer-based covariate temporal point process (TransFeat-TPP) model to improve the interpretability of deep covariate-TPPs while maintaining powerful expressiveness. TransFeat-TPP can effectively model complex relationships between events and covariates, and provide enhanced interpretability by discerning the importance of various covariates. Experimental results on synthetic and real datasets demonstrate improved prediction accuracy and consistently interpretable feature importance when compared to existing deep covariate-TPPs. Our code is available at https://github.com/waystogetthere/TransFeat.git.
dc.language	en
dc.publisher	IOS Press
dc.relation.ispartof	Frontiers in Artificial Intelligence and Applications
dc.relation.ispartofseries	Frontiers in Artificial Intelligence and Applications
dc.relation.isbasedon	10.3233/FAIA240566
dc.rights	info:eu-repo/semantics/openAccess
dc.title	TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes
dc.type	Conference Proceeding
utslib.citation.volume	392
pubs.organisational-group	University of Technology Sydney
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/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.date.updated	2025-02-06T04:20:26Z
pubs.publication-status	Published
pubs.volume	392

Abstract:

The classical temporal point process (TPP) constructs an intensity function by taking the occurrence times into account. Nevertheless, occurrence time may not be the only relevant factor, other contextual data, termed covariates, may also impact the event evolution. Incorporating such covariates into the model is beneficial, while distinguishing their relevance to the event dynamics is of great practical significance. In this work, we propose a Transformer-based covariate temporal point process (TransFeat-TPP) model to improve the interpretability of deep covariate-TPPs while maintaining powerful expressiveness. TransFeat-TPP can effectively model complex relationships between events and covariates, and provide enhanced interpretability by discerning the importance of various covariates. Experimental results on synthetic and real datasets demonstrate improved prediction accuracy and consistently interpretable feature importance when compared to existing deep covariate-TPPs. Our code is available at https://github.com/waystogetthere/TransFeat.git.

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http://hdl.handle.net/10453/184998