Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting

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 represe...

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Bibliographic Details
Main Authors: Juhyeon Kim, Hyungeun Lee, Seungwon Yu, Ung Hwang, Wooyeol Jung, Kijung Yoon
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Time series analysis
long sequence time series forecast
graph neural network
structure learning
self-attention
Online Access:https://ieeexplore.ieee.org/document/10286512/

Internet

https://ieeexplore.ieee.org/document/10286512/

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