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<p>Accurate prediction of corn futures prices is critical for global food security and agricultural risk management, yet existing deep learning models struggle to fully exploit residual patterns and integrate macroeconomic uncertainty factors effectively. We propose a three-stage hierarchical ensemble framework that synergizes Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks for complementary temporal modeling, followed by Extreme Gradient Boosting (XGBoost) for residual correction with multi-source feature fusion. Hyperparameters were systematically optimized via Optuna framework. Empirical evaluation on 1,196 trading days of Chinese corn futures data (March 2018 to February 2023) demonstrates that our approach achieves R² = 0.9286, representing a 2.69% improvement over simple averaging (R² = 0.9018) and 1.28% over the best single model (R² = 0.9167). Statistical significance is confirmed via Diebold-Mariano tests (<i>p</i> &lt; 0.001), with RMSE reduced by 14.77%. Ablation studies reveal that macroeconomic factors, particularly interest rates (44.94%) and geopolitical risk (15.15%), contribute over 60% to predictive power. This framework provides a generalizable methodology for financial time series forecasting and demonstrates the critical role of uncertainty quantification in ensemble learning.</p>

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A stacked ensemble of LSTM, GRU and XGBoost with residual learning for corn futures price forecasting

  • 期刊论文

  • 发表日期：

    2026年2月19日

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  • Xin-Jiang He,
  • Zezhou Chen,
  • Sha Lin

摘要

Accurate prediction of corn futures prices is critical for global food security and agricultural risk management, yet existing deep learning models struggle to fully exploit residual patterns and integrate macroeconomic uncertainty factors effectively. We propose a three-stage hierarchical ensemble framework that synergizes Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks for complementary temporal modeling, followed by Extreme Gradient Boosting (XGBoost) for residual correction with multi-source feature fusion. Hyperparameters were systematically optimized via Optuna framework. Empirical evaluation on 1,196 trading days of Chinese corn futures data (March 2018 to February 2023) demonstrates that our approach achieves R² = 0.9286, representing a 2.69% improvement over simple averaging (R² = 0.9018) and 1.28% over the best single model (R² = 0.9167). Statistical significance is confirmed via Diebold-Mariano tests (p < 0.001), with RMSE reduced by 14.77%. Ablation studies reveal that macroeconomic factors, particularly interest rates (44.94%) and geopolitical risk (15.15%), contribute over 60% to predictive power. This framework provides a generalizable methodology for financial time series forecasting and demonstrates the critical role of uncertainty quantification in ensemble learning.