Conventional time series forecasting approaches often face challenges in modeling intricate multivariate correlations and fail to jointly capture local temporal dynamics as well as long-range dependencies. To overcome these issues, we propose a novel forecasting framework that leverages multi-granularity feature extraction based on graph neural network (GNN). Our approach integrates information at the node, edge, and subgraph levels to construct a comprehensive representation that encompasses both fine-grained and global structures in multivariate time series data. A Graph Attention Network (GAT) is employed to adaptively assign importance weights between nodes and their neighbors, enabling the model to effectively capture complex spatial–temporal interactions. Extensive experiments conducted on four benchmark datasets across multiple prediction horizons demonstrate that our method consistently outperforms existing baselines in predictive accuracy. Beyond accuracy improvements, the model’s ability to represent structural intricacies of time series data enhances its applicability to a wide range of forecasting scenarios across diverse domains.