A Novel BiGRU-Attention Model for Predicting Corn Market Prices Based on Multi-Feature Fusion and Grey Wolf Optimization

Yang Feng, Xiaonan Hu, Songsong Hou and Yan Guo ()
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Yang Feng: College of Information Engineering, Sichuan Agricultural University, Chengdu 611130, China
Xiaonan Hu: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Songsong Hou: College of Information Engineering, Sichuan Agricultural University, Chengdu 611130, China
Yan Guo: College of Information Engineering, Sichuan Agricultural University, Chengdu 611130, China

Agriculture, 2025, vol. 15, issue 5, 1-25

Abstract: Accurately predicting corn market prices is crucial for ensuring corn production, enhancing farmers’ income, and maintaining the stability of the grain market. However, corn price fluctuations are influenced by various factors, exhibiting non-stationarity, nonlinearity, and high volatility, making prediction challenging. Therefore, this paper proposes a comprehensive, efficient, and accurate method for predicting corn prices. First, in the data processing phase, the seasonal and trend decomposition using LOESS (STL) algorithm was used to extract the trend, seasonality, and residual components of corn prices, combined with the GARCH-in-mean (GARCH-M) model to delve into the volatility clustering characteristics. Next, the kernel principal component analysis (KPCA) was employed for nonlinear dimensionality reduction to extract key information and accelerate model convergence. Finally, a BiGRU-Attention model, optimized by the grey wolf optimizer (GWO), was constructed to predict corn market prices accurately. The effectiveness of the proposed model was assessed through cross-sectional and longitudinal validation experiments. The empirical results indicated that the proposed STLG-KPCA-GWO-BiGRU-Attention (SGKGBA) model exhibited significant advantages in terms of MAE (0.0159), RMSE (0.0215), MAPE (0.5544%), and R 2 (0.9815). This model effectively captures price fluctuation features, significantly enhances prediction accuracy, and offers reliable trend forecasts for decision makers regarding corn market prices.

Keywords: digital agriculture; feature extraction; KPCA; GWO; BiGRU-Attention (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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