@inproceedings{
kim2023hierarchical,
title={Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting},
author={JuHyeon Kim and HyunGeun Lee and Seungwon Yu and Ung Hwang and Wooyul Jung and Miseon Park and Kijung Yoon},
booktitle={Temporal Graph Learning Workshop @ NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=GYSG2vF6z5}
}
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 multivariate signals as nodes in a graph with edges indicating interdependency between them. Specifically, we leverage graph neural networks (GNN) and attention mechanisms to efficiently learn the underlying relationships within the time series data. Moreover, we suggest employing hierarchical signal decompositions running over the graphs to capture multiple spatial dependencies. The effectiveness of our proposed model is evaluated across various real-world benchmark datasets designed for long-term forecasting tasks. The results consistently showcase the superiority of our model, achieving an average 23\% reduction in mean squared error (MSE) compared to existing models.
Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting
JuHyeon Kim HyunGeun Lee Seungwon Yu Ung Hwang Wooyul Jung Miseon Park Kijung Yoon