A Dynamic Stiefel Graph Neural Network for Efficient Spatio-Temporal Time Series Forecasting

Exploring foci of: arXiv (Cornell University) A Dynamic Stiefel Graph Neural Network for Efficient Spatio-Temporal Time Series Forecasting June 2025 • J. P. Zheng, Liang Xie 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 … Open Article Page

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