{"title":"一种新的数据驱动模型用于可解释的生猪价格预测","authors":"Binrong Wu, Huanze Zeng, Huanling Hu, Lin Wang","doi":"10.1007/s10489-025-06323-6","DOIUrl":null,"url":null,"abstract":"<div><p>Forecasting hog prices is an important and challenging task for pig producers and managers as it plays a crucial role in decision-making processes. Given the significant impact of raw pork supply, public concern, animal diseases, and international markets on hog prices, this study proposes a comprehensive and explainable hybrid model for hog price forecasting by combining principal component analysis (PCA), variational mode decomposition (VMD), weighted average algorithm (WAA) algorithm, and temporal fusion transformers (TFT). To improve the quality of input variables, search engine data reflecting public concern about live pig prices are dimensionally reduced using PCA. This reduction process helps in eliminating unnecessary information and enhancing the input’s relevance. Additionally, VMD is applied to decompose raw pig futures prices, enabling the capture of their underlying trends over time. Subsequently, all the input variables, including the processed search engine data and the decomposed pig futures prices, are fed into the WAA-TFT model. WAA algorithm optimizes the parameters of the TFT model, resulting in accurate predicted values. The interpretable nature of the TFT model provides valuable decision-making insights for practitioners in the agricultural products market. The experimental results show that the proposed model achieves a mean absolute percentage error (MAPE) of only 1.76% on the Chinese hog price prediction dataset, demonstrating the excellent predictive performance of the proposed model.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel data-driven model for explainable hog price forecasting\",\"authors\":\"Binrong Wu, Huanze Zeng, Huanling Hu, Lin Wang\",\"doi\":\"10.1007/s10489-025-06323-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Forecasting hog prices is an important and challenging task for pig producers and managers as it plays a crucial role in decision-making processes. Given the significant impact of raw pork supply, public concern, animal diseases, and international markets on hog prices, this study proposes a comprehensive and explainable hybrid model for hog price forecasting by combining principal component analysis (PCA), variational mode decomposition (VMD), weighted average algorithm (WAA) algorithm, and temporal fusion transformers (TFT). To improve the quality of input variables, search engine data reflecting public concern about live pig prices are dimensionally reduced using PCA. This reduction process helps in eliminating unnecessary information and enhancing the input’s relevance. Additionally, VMD is applied to decompose raw pig futures prices, enabling the capture of their underlying trends over time. Subsequently, all the input variables, including the processed search engine data and the decomposed pig futures prices, are fed into the WAA-TFT model. WAA algorithm optimizes the parameters of the TFT model, resulting in accurate predicted values. The interpretable nature of the TFT model provides valuable decision-making insights for practitioners in the agricultural products market. The experimental results show that the proposed model achieves a mean absolute percentage error (MAPE) of only 1.76% on the Chinese hog price prediction dataset, demonstrating the excellent predictive performance of the proposed model.</p></div>\",\"PeriodicalId\":8041,\"journal\":{\"name\":\"Applied Intelligence\",\"volume\":\"55 6\",\"pages\":\"\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2025-02-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10489-025-06323-6\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Intelligence","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10489-025-06323-6","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A novel data-driven model for explainable hog price forecasting
Forecasting hog prices is an important and challenging task for pig producers and managers as it plays a crucial role in decision-making processes. Given the significant impact of raw pork supply, public concern, animal diseases, and international markets on hog prices, this study proposes a comprehensive and explainable hybrid model for hog price forecasting by combining principal component analysis (PCA), variational mode decomposition (VMD), weighted average algorithm (WAA) algorithm, and temporal fusion transformers (TFT). To improve the quality of input variables, search engine data reflecting public concern about live pig prices are dimensionally reduced using PCA. This reduction process helps in eliminating unnecessary information and enhancing the input’s relevance. Additionally, VMD is applied to decompose raw pig futures prices, enabling the capture of their underlying trends over time. Subsequently, all the input variables, including the processed search engine data and the decomposed pig futures prices, are fed into the WAA-TFT model. WAA algorithm optimizes the parameters of the TFT model, resulting in accurate predicted values. The interpretable nature of the TFT model provides valuable decision-making insights for practitioners in the agricultural products market. The experimental results show that the proposed model achieves a mean absolute percentage error (MAPE) of only 1.76% on the Chinese hog price prediction dataset, demonstrating the excellent predictive performance of the proposed model.
期刊介绍:
With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance.
The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.