考虑产品特征属性的替代性需求预测方法

doi:10.12066/j.issn.1007-2861.2173

上海大学学报(自然科学版) ›› 2021, Vol. 27 ›› Issue (3): 573-582.doi: 10.12066/j.issn.1007-2861.2173

考虑产品特征属性的替代性需求预测方法

高峻峻(), 倪子玥

上海大学悉尼工商学院, 上海 201800

收稿日期:2018-10-10 出版日期:2021-06-30 发布日期:2021-06-27
通讯作者: 高峻峻 E-mail:gaojunjun@shu.edu.cn
作者简介:高峻峻(1976—), 女, 教授, 博士, 研究方向为需求链建模与优化. E-mail: gaojunjun@shu.edu.cn
基金资助:
国家自然科学基金资助项目(71871133)

Alternative demand forecasting considering product feature attribute

GAO Junjun(), NI Ziyue

SILC Business School, Shanghai University, Shanghai 201800, China

Received:2018-10-10 Online:2021-06-30 Published:2021-06-27
Contact: GAO Junjun E-mail:gaojunjun@shu.edu.cn

摘要/Abstract

摘要：

随着电商销售业务的高速发展, 对用户需求进行快速准确预测已成为重要的研究方向. 产品间的替代性对需求有一定影响作用, 且此方面的应用研究在不断深入. 为了提升需求预测精度, 基于畅销预测属性值排序, 利用邻近替代率估计方法, 并结合 Adaboost 预测模型, 构建出一种更优的考虑产品特征属性的替代性需求预测方法, 并通过实验证明该方法行之有效.

关键词: 需求预测, 替代率, 逻辑回归, 自适应提升

Abstract:

The current e-commerce sales business is developing rapidly, and the rapid and accurate prediction of demands has become a necessary research direction. The substitution of products has a significant influence on demand, and applied research in this aspect is increasing. Based on the ranking of best-selling predictive attribute values, proximity replacement rate estimation and the Adaboost prediction model were applied in this study to develop an improved demand forecasting method with higher accuracy, considering product feature attributes. The experimental findings confirm that the proposed method is accurate and reliable.

Key words: demand forecast, substitution rate, logistic regression, adaptive boosting

中图分类号:

TP391

高峻峻, 倪子玥. 考虑产品特征属性的替代性需求预测方法[J]. 上海大学学报(自然科学版), 2021, 27(3): 573-582.

GAO Junjun, NI Ziyue. Alternative demand forecasting considering product feature attribute[J]. Journal of Shanghai University（Natural Science Edition）, 2021, 27(3): 573-582.

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考虑产品特征属性的替代性需求预测方法

Alternative demand forecasting considering product feature attribute

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[2]	高峻峻1，谭崇利1，刘悦2，尹亚锋2. 一类产品需求预测的支持向量机方法[J]. 上海大学学报(自然科学版), 2009, 15(1): 71-76.