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

• S. Kayalvizhi,
• S. Nagarajan,
• A. Moorthy,
• M. Kathiravan,
• K. Vijaya Bhaskar,

Vol. 20, No. 2, February 28, 2026
10.3837/tiis.2026.02.003, Download Paper (Free):

• Adaptive Ensemble Meta-Learning
• Stochastic Gradient Descent
• concept drift detection
• Non-Stationary Financial Markets
• Real-Time Prediction

Abstract

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.

Statistics

Cite this article