Dynamic Periodic Event Graphs for multivariate time series pattern prediction

Understanding and predicting outcomes in complex real-world systems necessitates robust multivariate time series pattern analysis. Advanced techniques, such as dynamic graph neural networks, have shown significant efficacy for these tasks. However, existing approaches often overlook the inherent per...

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Détails bibliographiques

Auteurs principaux:	SoYoung Park, HyeWon Lee, Sungsu Lim
Format:	Artigo
Langue:	Inglês
Publié:	PeerJ Inc. 2025-02-01
Collection:	PeerJ Computer Science
Sujets:	Multivariate time series analysis Dynamic graphs Event graphs Graph neural networks Self-supervised learning Link prediction
Accès en ligne:	https://peerj.com/articles/cs-2717.pdf
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