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A novel data-driven model for explainable hog price forecasting
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AI
DOI：10.1007/s10489-025-06323-6.png)
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Abstract
En 中文
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.
Keywords:
Agricultural product price forecasting
Interpretable forecasting model
Decomposition method
Evolutionary algorithm
Deep learning
Journal
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