Dynamic risk prediction in financial-production systems using temporal self-attention and adaptive autoregressive models - Xuduo Lin; Ziang Qi (Frontiers in Physics)
In financial production systems, accurate risk prediction is crucial for decision- makers. Traditional forecasting methods face certain limitations when dealing with complex time-series data and nonlinear dependencies between systems, especially under extreme market fluctuations. To address this, we propose an innovative hybrid temporal model, TSA-AR (Temporal Self-Attention Adaptive Autoregression), which combines temporal self-attention mechanisms with an adaptive autoregressive model to solve the risk prediction problem in financial and production systems. TSA-AR performs multi-scale feature extraction through an improved Informer encoder, dynamically adjusts model parameters with a dynamic autoregressive module, and constructs the nonlinear dependencies between financial and production systems through a cross_modal interaction graph. Experimental results show that TSA-AR achieves an MSE of 0.0689, significantly lower than other comparative models (e.g., Transformer’s 0.0921), and performs excellently with an Extreme Risk Detection Rate of 81.70%. The model effectively improves the accuracy and stability of risk prediction, providing a more accurate forecasting tool for financial-production system risk management, with significant practical implications.