Modeling Grain Futures Prices Through Uncertainty Indices and Mixed‐Frequency Fusion: An Interpretable Deep Learning Framework
研究开发了一个可解释的混频特征交互深度学习网络（IMF-FIDNet），通过整合低频不确定性指数和高频谷物价格数据，提升期货价格预测的准确性和稳健性，并量化各指标的贡献。
ABSTRACT This study innovatively develops an interpretable mixed‐frequency feature interaction deep learning network (IMF‐FIDNet) to improve high‐frequency grain futures price prediction via effective multi‐frequency data integration, with a focus on ensuring robustness amid market uncertainty. By refining advanced mixed‐frequency processing methods, proposing a new deep learning model, and integrating multiple modules, IMF‐FIDNet enhances feature interaction modeling between low‐frequency uncertainty indicators and high‐frequency grain prices. Experiments show it outperforms traditional models in accuracy and robustness, and effectively supports investment decisions; further, its interpretability quantifies uncertainty indices' contributions, confirming macro‐indicators' role in high‐frequency price forecasting.