Dynamic risk prediction in financial-production systems using temporal self-attention and adaptive autoregressive models
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...
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| Format: | Article |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2025.1627551/full |
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| author | Xuduo Lin Ziang Qi |
| author_facet | Xuduo Lin Ziang Qi |
| author_sort | Xuduo Lin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9a970cc8cee14a6ea4ab34db25e56315 |
| institution | DOAJ |
| issn | 2296-424X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj-art-9a970cc8cee14a6ea4ab34db25e563152025-08-20T02:44:08ZengFrontiers Media S.A.Frontiers in Physics2296-424X2025-07-011310.3389/fphy.2025.16275511627551Dynamic risk prediction in financial-production systems using temporal self-attention and adaptive autoregressive modelsXuduo Lin0Ziang Qi 1School of Environment, Education and Development, University of Manchester, Manchester, United KingdomDuke University, Durham, NC, United StatesIn 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.https://www.frontiersin.org/articles/10.3389/fphy.2025.1627551/fullrisk predictiontemporal self-attentionadaptive autoregression modelcross-modal interaction graphfinancial-production system |
| spellingShingle | Xuduo Lin Ziang Qi Dynamic risk prediction in financial-production systems using temporal self-attention and adaptive autoregressive models Frontiers in Physics risk prediction temporal self-attention adaptive autoregression model cross-modal interaction graph financial-production system |
| title | Dynamic risk prediction in financial-production systems using temporal self-attention and adaptive autoregressive models |
| title_full | Dynamic risk prediction in financial-production systems using temporal self-attention and adaptive autoregressive models |
| title_fullStr | Dynamic risk prediction in financial-production systems using temporal self-attention and adaptive autoregressive models |
| title_full_unstemmed | Dynamic risk prediction in financial-production systems using temporal self-attention and adaptive autoregressive models |
| title_short | Dynamic risk prediction in financial-production systems using temporal self-attention and adaptive autoregressive models |
| title_sort | dynamic risk prediction in financial production systems using temporal self attention and adaptive autoregressive models |
| topic | risk prediction temporal self-attention adaptive autoregression model cross-modal interaction graph financial-production system |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2025.1627551/full |
| work_keys_str_mv | AT xuduolin dynamicriskpredictioninfinancialproductionsystemsusingtemporalselfattentionandadaptiveautoregressivemodels AT ziangqi dynamicriskpredictioninfinancialproductionsystemsusingtemporalselfattentionandadaptiveautoregressivemodels |