A lightweight Spatial-Temporal Graph Neural Network for Long-term Time Series Forecasting
This work addresses the problem of efficient and accurate long-term forecasting for multivariate time series, offering a compact and interpretable framework that is incremental over existing graph-based and decomposition methods.
The paper tackles long-term multivariate time series forecasting by proposing Lite-STGNN, a lightweight spatial-temporal graph neural network that integrates decomposition-based temporal modeling with learnable sparse graph structure, achieving state-of-the-art accuracy on four benchmark datasets for horizons up to 720 steps while being parameter-efficient and faster to train than transformer-based methods.
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.