ORIGINAL RESEARCH article
Front. Phys.
Sec. Social Physics
Volume 13 - 2025 | doi: 10.3389/fphy.2025.1627551
Dynamic Risk Prediction in Financial-Production Systems Using Temporal Self-Attention and Adaptive Autoregressive Models
Provisionally accepted
Xuduo Lin1*
Ziang Qi2- 1The University of Manchester, Manchester, United Kingdom
- 2Duke University, Durham, United States
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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 (ERDR) 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.
Keywords: risk prediction, Temporal Self-Attention, Adaptive Autoregression Model, Cross-Modal Interaction Graph, Financial-Production System
Received: 13 May 2025; Accepted: 18 Jun 2025.
Copyright: © 2025 Lin and Qi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xuduo Lin, The University of Manchester, Manchester, United Kingdom
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