Enhancing the Rice Price Forecasting Through Holt-Winters-GRU Hybrid Model: Evidence from Global Market Data

Abstract

This paper presents a detailed analysis of the Holt-Winters-GRU hybrid model for predicting global rice prices, an essential agricultural commodity. The benefits of the traditional statistical approaches are combined with deep learning power, and the results have been found to outperform a standalone GRU. The hybrid model produced a test RMSE of 27.7532 with almost no difference between the training and testing errors, thus showing robust generalization ability. Detailed scrutiny of the weight heat map for the GRU layer reflects the intricacies of the model while depicting both seasonal patterns and intricate nonlinear relationships present in the rice price time series. The findings from the study reveal that the Holt-Winters-GRU hybrid model is usable in forecasting rice price movements for policymakers, traders, and market analysts, considering its ability to handle systematic trends and shocks. Recommendations for model implementation, enhancement, risk management, policy applications, and future research are provided to extend further the utility of this hybrid forecasting approach in agricultural commodity markets.