FAST-GNN: a multivariate time series prediction method based on spatiotemporal adaptive graph neural network

Abstract

This paper proposes FAST-GNN, a graph neural network that integrates frequency-aware and spatio-temporal adaptive mechanisms to address the challenges of modeling complex spatio-temporal dependencies in multivariate time series forecasting. Traditional GNNs often struggle with dynamic graph construction, frequency-domain feature extraction, and noise robustness. Our approach innovatively introduces a multi-band driven dynamic graph construction mechanism to finely capture multi-scale correlations, employs a dual-granularity adaptive graph filter to distinguish and integrate local and global dependencies, and adopts a hierarchical graph reading strategy to enhance robustness against noise and anomalies. Extensive experiments on real-world datasets, including Climate, Electricity, Weather, and PEMS series, demonstrate that FAST-GNN significantly outperforms state-of-the-art baselines in both long- and short-term forecasting tasks, particularly showing stable and superior performance in challenging scenarios such as high-noise environments and long-term horizons.