Explainable Ensemble Forecasting of Multi-Commodity Agricultural Futures Prices via Reinforcement Learning and Chaotic Evolution Optimization

Forecasting agricultural futures prices across multiple commodities remains highly challenging due to nonlinear price dynamics, strong market volatility, and complex cross-market and cross-commodity interactions. To address these issues, this paper proposes an explainable ensemble forecasting framework that integrates cross-market feature construction and interpretation, a benchmark forecasting model pool, and reinforcement learning-based ensemble optimization. A diverse set of deep learning models generate benchmark forecasts using cross-market features that contain effective information, and a reinforcement learning-guided chaotic evolutionary optimization algorithm is employed to dynamically determine the ensemble weights under a multi-objective criterion that balances prediction accuracy and stability. Meanwhile, explainable artificial intelligence techniques, including SHAP and LIME, are incorporated to analyze the contribution of cross-market features to price predictions. The proposed framework is applied to eight major agricultural futures markets, including wheat, corn, and soybean-related commodities. Empirical results show that the ensemble model consistently outperforms individual forecasting models in terms of prediction accuracy and stability. Moreover, in backtesting, most agricultural commodities achieve positive returns under controlled risk levels. The findings indicate that the proposed framework not only improves predictive performance and generates substantial returns for most commodities, but also provides interpretable insights into the mechanisms linking financial markets and agricultural commodity prices, demonstrating its potential value for forecasting, trading, and risk management applications.