Optimizing Precision in Volatile Crypto Markets with E²-Fuse: An Energy-Based Ensemble for Bitcoin Forecasting
DOI:
https://doi.org/10.71204/f1dbs667Keywords:
Bitcoin Price Prediction, Ensemble Learning, Machine Learning, Energy Minimization, Gradient-Based Optimization, Financial ForecastingAbstract
Bitcoin (BTC) has become a major financial asset, attracting significant attention from both institutional and individual investors. However, its extreme price volatility—driven by macroeconomic factors, regulatory changes, and investor sentiment—makes accurate forecasting challenging, as traditional models and machine learning approaches struggle to effectively capture its complex dynamics. Addressing these limitations, we introduce E²-Fuse, a novel energy-minimizing ensemble framework specifically designed for Bitcoin price prediction. E²-Fuse conceptualizes each base model’s mean squared error (MSE) as individual “energy” components and employs gradient-based optimization techniques to minimize the total system energy, thereby enhancing predictive accuracy. This physics-inspired methodology systematically integrates multiple advanced ML predictors, ensuring that the ensemble adapts dynamically to BTC’s volatile market conditions. Our empirical evaluations demonstrate that E²-Fuse achieves a Normalized RMSE (Accuracy Score) of 0.9969, indicating near-perfect prediction accuracy, and consistently outperforms single models and traditional ensemble methods by achieving lower prediction errors and enhanced robustness against market fluctuations. The framework’s model-agnostic nature allows for seamless incorporation of diverse algorithms, including neural networks and gradient boosting methods, broadening its applicability across various high-volatility domains beyond finance. By formalizing ensemble weight determination through energy minimization, E²-Fuse not only advances the state-of-the-art in BTC forecasting but also offers a versatile and theoretically grounded approach for optimizing predictive performance in complex, dynamic environments. This study underscores the potential of integrating advanced ML techniques within an optimization-driven ensemble framework, paving the way for more reliable and adaptive financial forecasting models.
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