A lightweight Spatial-Temporal Graph Neural Network for Long-term Time Series Forecasting

• URL: http://arxiv.org/abs/2512.17453v1
• Date: Fri, 19 Dec 2025 11:12:20 GMT
• Title: A lightweight Spatial-Temporal Graph Neural Network for Long-term Time Series Forecasting
• Abstract summary: Lite-STGNN is a lightweight spatial-temporal graph neural network for long-term multivariate forecasting.<n>It integrates decomposition-based temporal modeling with learnable sparse graph structure.<n> Lite-STGNN achieves state-of-the-art accuracy on four benchmark datasets for horizons up to 720 steps.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We propose Lite-STGNN, a lightweight spatial-temporal graph neural network for long-term multivariate forecasting that integrates decomposition-based temporal modeling with learnable sparse graph structure. The temporal module applies trend-seasonal decomposition, while the spatial module performs message passing with low-rank Top-$K$ adjacency learning and conservative horizon-wise gating, enabling spatial corrections that enhance a strong linear baseline. Lite-STGNN achieves state-of-the-art accuracy on four benchmark datasets for horizons up to 720 steps, while being parameter-efficient and substantially faster to train than transformer-based methods. Ablation studies show that the spatial module yields 4.6% improvement over the temporal baseline, Top-$K$ enhances locality by 3.3%, and learned adjacency matrices reveal domain-specific interaction dynamics. Lite-STGNN thus offers a compact, interpretable, and efficient framework for long-term multivariate time series forecasting.

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