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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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, to rely on simplified metrics such as cosine similarity, or to patch the MTS.
“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]
The FC Graph Construction section says the method segments each MTS sample into patches and uses a truncation operation to calculate their number. It also calls the dot product a simple metric for sensor similarity. These statements contradict the claim that the method avoids patching and simplified similarity metrics; the cited paper describes its method as FC-STGNN, not TVG. (LLM, conf=0.90)
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Dynamic multi-periodic spatio-temporal graph neural network for multivariate time series forecasting
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A Few-Shot Multi-Energy Demand Forecasting Method Based on Meta-Learning Graph Neural Network
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ECFCON: Emotion Consequence Forecasting in Conversations
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Diffusion-Guided Diversity for Single Domain Generalization in Time Series Classification
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Disease and personality information enhanced depression detection based on the TransGCL framework
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Self-citationSpatio-Temporal Wavelet Enhanced Attention Mamba for Stock Price Forecasting
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TCGPN: Temporal-Correlation Graph Pre-trained Network for Stock Forecasting
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Receptive Field Aware Contrastive Learning for Multivariate Time Series Classification
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FST-AD: Anomaly Detection for Cyber-Physical Systems via Frequency-Spatio-Temporal GNNs
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Graph Structure Learning via Transfer Entropy for Multivariate Time Series Anomaly Detection
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A Multitask Learning Model for Temperature and Relative Humidity Prediction in Multi-zone Buildings
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Spatiotemporal Dynamic Graph Isomorphism Network for Satellite Image Time Series Classification
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An Attention-Based Spatio-Temporal Graph Convolutional Network for Gas Concentration PredictionPreprint
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Self-citationFederated Subgraph Learning over Spatio-Temporal Graphs for Traffic Prediction
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Spatial-Temporal Traffic Prediction Based on Multi-Scale Time Difference
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Self-citationTemporal and spatial heterogeneity of antarctic krill abundance in relation to seawater temperature
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Structural Entropy-based Multivariate Time Series Forecasting
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Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data
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Self-citation
- 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
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Keywords
- multivariate time series, spatio-temporal graph neural networks, sensor correlation, temporal correlation, graph convolution
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