Cited by 134Time Series Analysis and ForecastingAdvanced Graph Neural NetworksTraffic Prediction and Management Techniques

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Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data

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Wang, Yucheng, Xu, Yuecong, Yang, Jianfei, Wu, Min, Li, Xiaoli, Xie, Lihua, Chen, Zhenghua·2024·Proceedings of the AAAI Conference on Artificial Intelligence

DOI 10.1609/aaai.v38i14.29500

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Abstract

Multivariate Time-Series (MTS) data is crucial in various application fields. With its sequential and multi-source (multiple sensors) properties, MTS data inherently exhibits Spatial-Temporal (ST) dependencies, involving temporal correlations between timestamps and spatial correlations between sensors in each timestamp. To effectively leverage this information, Graph Neural Network-based methods (GNNs) have been widely adopted. However, existing approaches separately capture spatial dependency and temporal dependency and fail to capture the correlations between Different sEnsors at Different Timestamps (DEDT). Overlooking such correlations hinders the comprehensive modelling of ST dependencies within MTS data, thus restricting existing GNNs from learning effective representations. To address this limitation, we propose a novel method called Fully-Connected Spatial-Temporal Graph Neural Network (FC-STGNN), including two key components namely FC graph construction and FC graph convolution. For graph construction, we design a decay graph to connect sensors across all timestamps based on their temporal distances, enabling us to fully model the ST dependencies by considering the correlations between DEDT. Further, we devise FC graph convolution with a moving-pooling GNN layer to effectively capture the ST dependencies for learning effective representations. Extensive experiments show the effectiveness of FC-STGNN on multiple MTS datasets compared to SOTA methods. The code is available at https://github.com/Frank-Wang-oss/FCSTGNN.

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Authors: Wang, Y., Xu, Y., Yang, J., Wu, M., Li, X., Xie, L., & Chen, Z.
Affiliations: Agency for Science, Technology and Research (SG)
Nanyang Technological University (SG)
ORCID: Wang, Yucheng
Xu, Yuecong
Yang, Jianfei
Wu, Min
Li, Xiaoli
Xie, Lihua
Chen, Zhenghua
Venue: Proceedings of the AAAI Conference on Artificial Intelligence
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Published: 2024-03-24
Volume: 38
Issue: 14
Pages: 15715-15724
ISSN: 2159-5399 (ISSN-L)
2374-3468
DOI: 10.1609/aaai.v38i14.29500
OpenAlex: W4393159722
Cited by: 134
References: 37
Citations by year: 2026: 46 · 2025: 66 · 2024: 22
FWCI: 50.25
Type: conference paper
Language: en
Open access: diamond
doi.org
Locations: Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
arXiv (Cornell University)
Funders: National Research Foundation (AISG2-RP-2021-027)
Agency for Science, Technology and Research (A20H6b0151, C210112046)
National Research Foundation Singapore (AISG2-RP-2021-027)
Topics: Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
Social Sciences › Transportation › Traffic Prediction and Management Techniques
Keywords: multivariate time series, spatio-temporal graph neural networks, sensor correlation, temporal correlation, graph convolution

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Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data

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