Spatiotemporal Risk Representation Learning Using Transformers and Graph Structure

This study addresses the challenges of financial risk early warning by proposing a modeling approach based on spatiotemporal Transformers. The research first examines the multidimensional characteristics of financial risk, emphasizing its temporal dynamics and cross-regional interactions. It notes that many existing methods struggle to jointly capture temporal dependencies and inter-regional risk transmission patterns. To overcome these limitations, a unified spatiotemporal modeling framework is developed. The framework integrates temporal encoding, spatial adjacency information, and multi-head attention mechanisms to model long-range dependencies and regional spillover effects. In the model architecture, an embedding layer is employed to learn representations from multi-source financial indicators. A self-attention mechanism facilitates global feature interaction, while a graph convolution component further enhances the modeling of spatial relationships across markets. The final risk representation is generated through a feed-forward network with normalization layers, providing a structured basis for financial risk assessment and early warning analysis. Experimental evaluations include comparative studies and sensitivity analyses under varying missing data ratios, time window settings, and environmental conditions. The results indicate that the proposed method consistently outperforms several baseline models in terms of accuracy, precision, recall, and F1-score. Overall, the approach demonstrates strong robustness and practical applicability in complex financial settings, offering an effective tool for financial risk monitoring and decision support.