Explainable Soybean Futures Price Forecasting Based on Multi-Source Feature Fusion

The prediction and early warning of soybean futures prices have been even more crucial for the formulation of food-related policies and trade risk management. Amid increasing geopolitical conflicts and uncertainty in trade policies across countries in recent years, there have been significant fluctuations in global soybean futures prices, making it necessary to investigate fluctuations in soybean futures prices, reveal the price determination mechanism, and accurately predict trends in future prices. Therefore, this study proposes a comprehensive and interpretable framework for soybean futures price forecasting. Specifically, this study employs a set of methodologies. Using a snow ablation optimizer (SAO), this study improves the parameters of a time fusion transformer (TFT) model, an advanced interpretable predictive model based on a self-attention mechanism. Besides, it addresses the factors influencing soybean futures prices and constructs effective fusion features through a feature fusion method. To explore volatility trends, the original soybean futures price series are decomposed using variational mode decomposition (VMD). This study also enhances the accuracy of soybean futures price predictions by introducing global geopolitical risk coefficients and trading volumes as predictors. The empirical findings suggest that the VMD-SAO-TFT model enhances prediction accuracy and interpretability, offering implications for decision-makers to achieve accurate predictions and early warning of agricultural futures prices.