A Vegetable-Price Forecasting Method Based on Mixture of Experts

文献类型： 外文期刊

第一作者： Zhao, Chenyun

作者： Zhao, Chenyun;Cui, Yunpeng;Wang, Ting;Liu, Juan;Hou, Ying;Wang, Mo;Chen, Li;Li, Huan;Wu, Jinming;Sun, Tan;Zhao, Chenyun;Cui, Yunpeng;Wang, Ting;Liu, Juan;Hou, Ying;Wang, Mo;Chen, Li;Li, Huan;Wu, Jinming;Sun, Tan;Wang, Xiaodong;Zhao, Anping

作者机构：

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

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 2 期

页码：

收录情况： SCI

摘要： 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.

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