2025-01-14-xie25a.md

title	booktitle	year	volume	series	month	publisher	pdf	url	software	openreview	abstract	layout	issn	id	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	address	container-title	genre	issued	extras
Multi-level Relational Learning with Synergistic Graphs for Multivariate Time Series Forecasting	Proceedings of the 16th Asian Conference on Machine Learning	2025	260	Proceedings of Machine Learning Research	0	PMLR	https://raw.githubusercontent.com/mlresearch/v260/main/assets/xie25a/xie25a.pdf	https://proceedings.mlr.press/v260/xie25a.html	https://github.com/YoungXiee/MSG	R1nIS77g3g	Multivariate Time Series (MTS) forecasting involves analyzing the evolution and interrelationships of multiple variables over time. Effectively mining relationships between MTS variables remains challenging as variables may imply multiple relational patterns. Recently, graph-based approaches have exhibited substantial effectiveness in capturing relationships between MTS variables. However, these methods often adhere to the paradigm of capturing low-level pairwise relationships, which limits their ability to capture other high-level beyond pair-wise relational patterns. To address this issue, we present a synergistic graph learning framework that combines the modeling advantages of graphs and hypergraphs to uncover more comprehensive relational patterns. This framework mainly consists of two parts. Firstly, we introduced a Synergistic Relation Construction module, which incorporates dynamic graph and hypergraph structures to model low-level pairwise and high-level beyond pairwise relationships among variables, representing multi-level relational patterns through obtained adjacency matrices and incidence metrics. Additionally, we developed a Synergistic Relation Learning mechanism, that leverages novel synergistic graph and hypergraph convolutional networks to facilitate spatial dependency interactions across multi-levels, along with temporal convolutional networks to capture more comprehensive spatial-temporal dependencies. We conducted comprehensive experiments on four benchmark datasets, and experimental results demonstrate that our model outperforms the state-of-the-art performance. The source code will be available online.	inproceedings	2640-3498	xie25a	Multi-level Relational Learning with Synergistic Graphs for Multivariate Time Series Forecasting	175	190	175-190	175	false	Nguyen, Vu and Lin, Hsuan-Tien	given family Vu Nguyen given family Hsuan-Tien Lin	Xie, Yang and Liu, Qiao and Hou, Rui and Dai, Tingting and Lan, Tian	given family Yang Xie given family Qiao Liu given family Rui Hou given family Tingting Dai given family Tian Lan	2025-01-14		Proceedings of the 16th Asian Conference on Machine Learning	inproceedings	date-parts 2025 1 14