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

2025-12-19

AI Summary

To address high model complexity and poor interpretability in long-term multivariate time series forecasting, this paper proposes Lite-STGNN, a lightweight spatiotemporal graph neural network. Methodologically, it introduces (1) a decomposition-driven lightweight spatiotemporal coupling architecture that jointly performs trend-seasonal decomposition and temporal modeling; and (2) a conservative temporal gating mechanism combined with low-rank Top-K sparse graph learning to preserve locality, enhance interpretability, and enable dynamic topology discovery. Evaluated on four benchmark datasets for 720-step-ahead forecasting, Lite-STGNN achieves state-of-the-art accuracy. It reduces parameter count by 62% and accelerates training by 3.8× over Transformer-based baselines. Ablation studies quantify performance gains of 4.6% and 3.3% attributable to the spatial module and Top-K graph learning, respectively.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Develops a lightweight graph neural network for long-term multivariate time series forecasting
• Integrates temporal decomposition with learnable sparse graph structures for spatial corrections
• Achieves high accuracy and efficiency while being interpretable and faster than transformers

Innovation

• Lightweight graph neural network with decomposition-based temporal modeling
• Learnable sparse graph structure using low-rank Top-K adjacency learning
• Conservative horizon-wise gating for efficient spatial corrections