A Vegetable-Price Forecasting Method Based on Mixture of Experts
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,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/2/162 |
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| _version_ | 1832589514248290304 |
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| author | Chenyun Zhao Xiaodong Wang Anping Zhao Yunpeng Cui Ting Wang Juan Liu Ying Hou Mo Wang Li Chen Huan Li Jinming Wu Tan Sun |
| author_facet | Chenyun Zhao Xiaodong Wang Anping Zhao Yunpeng Cui Ting Wang Juan Liu Ying Hou Mo Wang Li Chen Huan Li Jinming Wu Tan Sun |
| author_sort | Chenyun Zhao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f8126c607ff5463586fb461e3522bac1 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-f8126c607ff5463586fb461e3522bac12025-01-24T13:15:57ZengMDPI AGAgriculture2077-04722025-01-0115216210.3390/agriculture15020162A Vegetable-Price Forecasting Method Based on Mixture of ExpertsChenyun Zhao0Xiaodong Wang1Anping Zhao2Yunpeng Cui3Ting Wang4Juan Liu5Ying Hou6Mo Wang7Li Chen8Huan Li9Jinming Wu10Tan Sun11Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaBeijing Digital Agriculture Rural Promotion Center, Beijing 101117, ChinaBeijing Digital Agriculture Rural Promotion Center, Beijing 101117, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaThe 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.https://www.mdpi.com/2077-0472/15/2/162vegetable-price forecastingtime-series forecastinglarge language modelsdeep learningmixture-of-experts |
| spellingShingle | Chenyun Zhao Xiaodong Wang Anping Zhao Yunpeng Cui Ting Wang Juan Liu Ying Hou Mo Wang Li Chen Huan Li Jinming Wu Tan Sun A Vegetable-Price Forecasting Method Based on Mixture of Experts Agriculture vegetable-price forecasting time-series forecasting large language models deep learning mixture-of-experts |
| title | A Vegetable-Price Forecasting Method Based on Mixture of Experts |
| title_full | A Vegetable-Price Forecasting Method Based on Mixture of Experts |
| title_fullStr | A Vegetable-Price Forecasting Method Based on Mixture of Experts |
| title_full_unstemmed | A Vegetable-Price Forecasting Method Based on Mixture of Experts |
| title_short | A Vegetable-Price Forecasting Method Based on Mixture of Experts |
| title_sort | vegetable price forecasting method based on mixture of experts |
| topic | vegetable-price forecasting time-series forecasting large language models deep learning mixture-of-experts |
| url | https://www.mdpi.com/2077-0472/15/2/162 |
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