Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting

Multivariate time series is prevalent in many scientific and industrial domains. Modeling multivariate signals is challenging due to their long-range temporal dependencies and intricate interactions-both direct and indirect. To confront these complexities, we introduce a method of representing multi...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE access 2023, Vol.11, p.118386-118394
Hauptverfasser:	Kim, Juhyeon, Lee, Hyungeun, Yu, Seungwon, Hwang, Ung, Jung, Wooyeol, Yoon, Kijung
Format:	Artikel
Sprache:	eng
Schlagworte:	Complexity theory Computational modeling Forecasting graph neural network Graph neural networks Graph theory Graphical representations long sequence time series forecast Machine learning Mathematical models Multivariate analysis Predictive models self-attention Signal resolution Spatial dependencies structure learning Time series Time series analysis
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!