摘要
在S省生鲜农产品物流需求预测分析领域,采用组合模型支持下的预测方法显得尤为重要。然而,相关数据存在整合不精确、预测精度不高等问题。基于此,通过主成分分析与多元回归模型的建立,探索数据源的高效整理方法,确保了数据质量与可靠性;利用Shepley值法优化组合预测模型的构造,增强了模型的适应性与准确性;以S省生鲜农产品物流为案例,通过数据来源整理、主成分回归模型的建立及需求预测,以及基于Shepley组合模型的预测,展现模型在实际应用中的有效性与准确性;提出具体建议,旨在通过模型优化与技术应用,提升物流需求预测的准确度,为S省生鲜农产品物流的高效管理与发展提供科学依据。In the field of fresh agricultural products logistics demand forecasting analysis in Province S, the forecasting method supported by combinatorial modeling is particularly important. However, there are problems such as imprecise integration and low prediction accuracy of related data. Based on this, the efficient collation method of data sources is explored through the establishment of principal component analysis and multiple regression model, which ensures the quality and reliability of the data;the Shepley value method is used to optimize the construction of the combined prediction model, which strengthens the adaptability and accuracy of the model;the fresh agricultural products logistics of S province is taken as a case study, through the collation of data sources, the establishment of the principal component regression model and the demand prediction and the prediction of the fresh agricultural products logistics of S province based on the Shepley combination model, to show the effectiveness and accuracy of the model in practical application and put forward specific recommendations, aiming to improve the accuracy of logistics demand forecasting through model optimization and technology application, to provide scientific basis for t
出处
《电子商务评论》
2024年第4期554-560,共7页
E-Commerce Letters
