A Vegetable-Price Forecasting Method Based on Mixture of Experts

Chenyun Zhao, Xiaodong Wang, Anping Zhao, Yunpeng Cui (), Ting Wang (), Juan Liu, Ying Hou, Mo Wang, Li Chen, Huan Li, Jinming Wu and Tan Sun
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Chenyun Zhao: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Xiaodong Wang: Beijing Digital Agriculture Rural Promotion Center, Beijing 101117, China
Anping Zhao: Beijing Digital Agriculture Rural Promotion Center, Beijing 101117, China
Yunpeng Cui: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Ting Wang: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Juan Liu: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Ying Hou: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Mo Wang: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Li Chen: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Huan Li: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Jinming Wu: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Tan Sun: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Agriculture, 2025, vol. 15, issue 2, 1-19

Abstract: The accurate forecasting of vegetable prices is crucial for policy formulation, market decisions, and agricultural market stability. Traditional time-series models often require manual parameter tuning and struggle to effectively handle the complex non-linear characteristics of vegetable price data, limiting their predictive accuracy. This study conducts a comprehensive analysis of the performance of traditional methods, deep learning approaches, and cutting-edge large language models in vegetable-price forecasting using multiple predictive performance metrics. Experimental results demonstrate that large language models generally outperform other methods, but do not have consistent performance for all kinds of vegetables across different time scales. As a result, we propose a novel vegetable-price forecasting method based on mixture of expert models (VPF-MoE), which combines the strengths of large language models and deep learning methods. Different from the traditional single model prediction method, VPF-MoE can dynamically adapt to the characteristics of different vegetable types, dynamically select the best prediction method, and significantly improve the accuracy and robustness of the prediction. In addition, we optimized the application of large language models in vegetable-price forecasting, offering a new technological pathway for vegetable-price prediction.

Keywords: vegetable-price forecasting; time-series forecasting; large language models; deep learning; mixture-of-experts (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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