One potential limitation of the spatial-temporal-decoupled masking approach is the challenge of capturing highly intricate and nonlinear spatiotemporal patterns that may exist in the data. While the decoupled masking strategy allows for the separate modeling of spatial and temporal heterogeneity, there may be cases where the interactions between spatial and temporal dimensions are crucial for accurate forecasting. To address this limitation, the approach could be further improved by incorporating cross-dimensional interactions in the masking process. This could involve designing more sophisticated masking patterns that consider the joint influence of spatial and temporal factors on each other. Additionally, exploring the use of attention mechanisms or graph neural networks to capture complex dependencies between spatial and temporal dimensions could enhance the framework's ability to capture intricate spatiotemporal patterns more effectively.