轻量级图网络提升长期多变量时间序列预测精度
A lightweight Spatial-Temporal Graph Neural Network for Long-term Time Series Forecasting
- 分解时序建模+可学习稀疏图结构，兼顾效率与表达力
- 在4个数据集上720步预测达顶尖性能，参数少训练快
- 图结构可解释，适合需要高效长程建模的场景
我们提出Lite-STGNN，一种面向长期多变量时间序列预测的轻量级时空图神经网络。该模型结合基于分解的时序建模与可学习稀疏图结构：时序模块采用趋势-季节性分解，空间模块通过低秩Top-K邻接矩阵学习和保守的时序门控机制进行消息传递，实现对强线性基线的空间修正。在四个基准数据集上，模型在长达720步的预测中达到当前最优性能，同时参数更少、训练速度远超基于Transformer的方法。消融实验表明，空间模块相较时序基线提升4.6%，Top-K使局部性增强3.3%，学习到的邻接矩阵揭示了领域特定的交互动态。因此，Lite-STGNN提供了一种紧凑、可解释且高效的长期多变量时间序列预测框架。
原文摘要 · Abstract (English)
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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