ADAPTIVE PREDICTION IN NON-STATIONARY FINANCIAL MARKETS USING ADAPTIVE ENSEMBLE META-LEARNING (AEML) WITH STOCHASTIC GRADIENT DESCENT (SGD)

Financial markets are inherently volatile, characterized by rapid and unpredictable shifts driven by a variety of economic, geopolitical, and psychological factors.Traditional machine learning models, which assume a stationary data distribution, often falter in such dynamic environments, particularly when faced with concept drift.This paper addresses the critical challenge of maintaining accurate and responsive predictions in non-stationary financial markets by proposing an Adaptive Ensemble Meta-Learning (AEML) framework integrated with Stochastic Gradient Descent (SGD).The primary objective is to develop a real-time adaptive prediction model that continuously adjusts to evolving market conditions without the need for frequent retraining.The novelty of the proposed methodology lies in its dynamic ensemble approach, where a reinforcement learning-based controller selects and weights base models optimized via SGD in response to incoming data.The AEML framework also incorporates a concept drift detection mechanism, ensuring timely reconfiguration of the ensemble to align with new market distributions.Compared to existing techniques, the proposed AEML-SGD method demonstrates a significant improvement, achieving a prediction accuracy of 98.16%.This is a notable enhancement over traditional models, particularly in handling abrupt and gradual shifts in data distribution.The analysis shows that the proposed method outperforms existing models by approximately 15-20% in terms of adaptability and robustness, offering a highly effective solution for real-time financial market prediction.