一种新的数据驱动模型用于可解释的生猪价格预测

IF 3.5 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Binrong Wu, Huanze Zeng, Huanling Hu, Lin Wang
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引用次数: 0

摘要

预测生猪价格对生猪生产者和管理者来说是一项重要而具有挑战性的任务,因为它在决策过程中起着至关重要的作用。考虑到生猪供应、公众关注、动物疾病和国际市场对生猪价格的重大影响,本研究提出了一种综合主成分分析(PCA)、变分模态分解(VMD)、加权平均算法(WAA)和时间融合变压器(TFT)的生猪价格预测混合模型。为了提高输入变量的质量,使用主成分分析法对反映公众对生猪价格关注的搜索引擎数据进行了降维处理。这种简化过程有助于消除不必要的信息并增强输入的相关性。此外,VMD用于分解生猪期货价格,从而能够捕获其随时间变化的潜在趋势。随后,所有输入变量,包括处理后的搜索引擎数据和分解后的生猪期货价格,都被输入到WAA-TFT模型中。WAA算法对TFT模型的参数进行优化,得到准确的预测值。TFT模型的可解释性为农产品市场的从业者提供了有价值的决策见解。实验结果表明,该模型在中国生猪价格预测数据集上的平均绝对百分比误差(MAPE)仅为1.76%,表明该模型具有良好的预测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
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.
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