Yu-Ling Hong

Mgstfn: multi-granularity spatio-temporal-frequency network for traffic flow forecasting

Experimental results on four real-world datasets demonstrate that the proposed MGSTFN achieves superior performance compared to state-of-the-art methods, and the computational efficiency analysis shows that it maintains a favorable balance between prediction accuracy and computational cost, indicating its suitability for large-scale traffic forecasting scenarios.