A Dynamic Stiefel Graph Neural Network for Efficient Spatio-Temporal Time Series Forecasting Article Swipe
J. P. Zheng
,
Liang Xie
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2506.00798
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2506.00798
Spatio-temporal time series (STTS) have been widely used in many applications. However, accurately forecasting STTS is challenging due to complex dynamic correlations in both time and space dimensions. Existing graph neural networks struggle to balance effectiveness and efficiency in modeling dynamic spatio-temporal relations. To address this problem, we propose the Dynamic Spatio-Temporal Stiefel Graph Neural Network (DST-SGNN) to efficiently process STTS. For DST-SGNN, we first introduce the novel Stiefel Graph Spectral Convolution (SGSC) and Stiefel Graph Fourier Transform (SGFT). The SGFT matrix in SGSC is constrained to lie on the Stiefel manifold, and SGSC can be regarded as a filtered graph spectral convolution. We also propose the Linear Dynamic Graph Optimization on Stiefel Manifold (LDGOSM), which can efficiently learn the SGFT matrix from the dynamic graph and significantly reduce the computational complexity. Finally, we propose a multi-layer SGSC (MSGSC) that efficiently captures complex spatio-temporal correlations. Extensive experiments on seven spatio-temporal datasets show that DST-SGNN outperforms state-of-the-art methods while maintaining relatively low computational costs.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2506.00798
- https://arxiv.org/pdf/2506.00798
- OA Status
- green
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All OpenAlex metadata
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https://doi.org/10.48550/arxiv.2506.00798Digital Object Identifier
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A Dynamic Stiefel Graph Neural Network for Efficient Spatio-Temporal Time Series ForecastingWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-06-01Full publication date if available
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J. P. Zheng, Liang XieList of authors in order
- Landing page
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https://arxiv.org/abs/2506.00798Publisher landing page
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https://arxiv.org/pdf/2506.00798Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2506.00798Direct OA link when available
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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