Enhancing Systemic Risk Forecasting with Deep Attention Models in Financial Time Series

This paper proposes a deep learning model for systemic risk identification and prediction in financial markets, integrating multi-scale convolutional structures, attention mechanisms, and residual connections. The model is designed to capture dynamic features at different temporal resolutions, enhance focus on key risk factors, and maintain training stability in complex financial data environments. Using the US Systemic Risk Dataset released by the Federal Reserve, the model transforms the task into a binary classification problem based on crisis-related indicators. All time series data are standardized, interpolated, and structured through sliding windows to preserve temporal continuity. Experimental comparisons show that the proposed model outperforms traditional methods such as MLP, CNN, Transformer, and BERT across accuracy, precision, and recall. Further analysis reveals the effectiveness of multi-scale and attention mechanisms in modeling complex dependencies and detecting early risk signals. Sensitivity experiments on convolutional kernel size demonstrate that integrating cross-feature information significantly improves performance, with 3x3 and 5x3 kernels showing optimal results. The training process exhibits fast convergence and low loss stability, confirming the model's robustness and efficiency. These findings validate the technical advantages of the proposed architecture in addressing the challenges of systemic risk modeling in high-dimensional and heterogeneous financial time series.