Interpretable Hybrid Machine Learning for Cryptocurrency Price Prediction: Integrating Chaos Theory, Quantile Regression, and Ensemble Optimization

Abstract

This paper presents a novel hybrid methodology for cryptocurrency price prediction that integrates chaos theory, quantile regression, and evolutionary optimization. We propose a three-stage framework: (1) chaos detection using the 0-1 test and phase-space reconstruction, (2) quantile-based rule generation via Particle Swarm Optimization (PSO), and (3) ensemble pruning for interpretable prediction intervals (PIs). The approach addresses critical limitations in existing methods by simultaneously modeling nonlinear market dynamics, heteroskedasticity, and prediction uncertainty. Evaluated on four major cryptocurrencies (Bitcoin SV, Maker, etc.), our model achieves 12-18% higher Prediction Interval Coverage Probability (PICP) compared to LSTM and XGBoost baselines, while maintaining an average rule set size below 10 rules per asset. The generated rules provide actionable insights into market regimes, demonstrating the framework's value for algorithmic trading and risk management in volatile financial environments.