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<p>Continual learning remains a critical challenge in financial decision-making due to catastrophic forgetting where models struggle to retain past knowledge while adapting to evolving market conditions. This study proposes an enhanced Dynamic Neuroplastic Networksarchitecture that integrates adaptive attention mechanisms, continual learning strategies, and hybrid econometric models to address the challenges of financial decision-making under dynamic conditions. By employing Markov Switching models, Threshold Autoregressive (TAR) models, and Smooth Transition Autoregressive (STAR) models, we capture regime shifts and asymmetries in financial time series. We further refine volatility estimation using Realized Volatility (RV) and Realized GARCH models while detecting abrupt structural breaks through the Barndorff-Nielsen and Shephard (BNS) test. To enhance predictive capabilities, Long Short-Term Memory (LSTM) networks and Support Vector Machines (SVM) are employed alongside adaptive DNN modules for classifying market conditions. Hyperparameter tuning and robust model validation, including statistical significance testing and confidence interval analysis, were systematically conducted to ensure the reliability of results. Critical real-world applications of the proposed framework are discussed, and model limitations are acknowledged to guide future research directions. Unlike traditional continual learning frameworks that suffer from catastrophic forgetting, our model leverages dynamic synaptic consolidation inspired by Elastic Weight Consolidation (EWC) principles while incorporating adaptive attention layers that prioritize salient market features in real-time. The findings underscore the critical role of integrating neuroplastic continual learning with econometric analysis for improving financial forecasting, risk management, and market stability strategies. </p>

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Dynamic Neuroplastic Networks for Financial Decision Making: A Self-Adaptive Approach for Mitigating Catastrophic Forgetting in Continual Learning

  • 期刊论文

  • 发表日期：

    2025年8月8日

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  • Shafeeq Ur Rahaman

摘要

Continual learning remains a critical challenge in financial decision-making due to catastrophic forgetting where models struggle to retain past knowledge while adapting to evolving market conditions. This study proposes an enhanced Dynamic Neuroplastic Networksarchitecture that integrates adaptive attention mechanisms, continual learning strategies, and hybrid econometric models to address the challenges of financial decision-making under dynamic conditions. By employing Markov Switching models, Threshold Autoregressive (TAR) models, and Smooth Transition Autoregressive (STAR) models, we capture regime shifts and asymmetries in financial time series. We further refine volatility estimation using Realized Volatility (RV) and Realized GARCH models while detecting abrupt structural breaks through the Barndorff-Nielsen and Shephard (BNS) test. To enhance predictive capabilities, Long Short-Term Memory (LSTM) networks and Support Vector Machines (SVM) are employed alongside adaptive DNN modules for classifying market conditions. Hyperparameter tuning and robust model validation, including statistical significance testing and confidence interval analysis, were systematically conducted to ensure the reliability of results. Critical real-world applications of the proposed framework are discussed, and model limitations are acknowledged to guide future research directions. Unlike traditional continual learning frameworks that suffer from catastrophic forgetting, our model leverages dynamic synaptic consolidation inspired by Elastic Weight Consolidation (EWC) principles while incorporating adaptive attention layers that prioritize salient market features in real-time. The findings underscore the critical role of integrating neuroplastic continual learning with econometric analysis for improving financial forecasting, risk management, and market stability strategies.