The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data
Open accessAbstract
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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“In particular, the use of the TVG algorithm to map the MTS dataset into graphs eliminates the need to censor or truncate some parts of the MTS relationships, as is the case with the constrained correlation approach used in [102]; to rely on simplified metrics such as cosine similarity, as is the case in [101]; or to patch the MTS, as is the case in [96].”
From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [96]
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Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
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Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
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Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
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Wang, Y., Wu, M., Li, X., Xie, L., & Chen, Z. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112449 · Cited by 96
Self-citationMa, C., Mu, R., Li, M., He, J., Hua, C., Wang, L., Liu, J., Totis, G., Yang, J., Liu, K., et al. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127812 · Cited by 62
Self-citationWu, J., He, D., Jin, Z., Zhao, M., Li, X., & Chen, Y. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.05.009 · Cited by 50
Self-citationQin, Y., Zhao, Y., Qi, J., & Mao, Y. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111420 · Cited by 47
Kuang, H., Deng, K., You, L., & Li, J. (2025) · Energy · doi:10.1016/j.energy.2025.135170 · Cited by 40
Metadata
- 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)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2024-03-24
- Volume
- 38
- Issue
- 14
- Pages
- 15715-15724
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- 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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