Advanced Textile Technology ›› 2022, Vol. 30 ›› Issue (2): 27-35.DOI: 10.19398/j.att.202104036

• Comprehensive Review • Previous Articles     Next Articles

Research progress on quantitative forecast methods of clothing sales

ZHENG Jinfenga, LUO Rongleib,c()   

  1. a. School of Fashion Design & Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
    b. School of International Education, Zhejiang Sci-Tech University, Hangzhou 310018, China
    c. Zhejiang Sickand Fashion Culture Research Center, Zhejiang Sci-Tech University, Hangzhou 310018, China
  • Received:2021-04-19 Online:2022-03-10 Published:2021-07-09
  • Contact: LUO Ronglei

服装销售定量预测方法研究进展

郑金峰a, 罗戎蕾b,c()   

  1. a.服装学院,浙江理工大学,杭州 310018
    b.国际教育学院, 浙江理工大学,杭州 310018
    c.浙江丝绸与时尚文化研究中心, 浙江理工大学, 杭州 310018
  • 通讯作者: 罗戎蕾
  • 作者简介:郑金峰(1994-),男,河南周口人,硕士研究生,主要从事服装销售预测方面的研究。
  • 基金资助:
    浙江理工大学浙江省丝绸与文化艺术研究中心培育课题(ZSFCRC202104PY);浙江省大学生科技创新活动计划暨新苗人才计划大学生科技成果推广项目(2021R406064)

Abstract:

The forecast of clothing sales is one of the essential steps in the commodity planning of clothing enterprises. In order to effectively help garment planners and relevant scholars to choose appropriate forecast methods of clothing sales quickly as the case may be, this study summarizes the advantages and disadvantages, optimization process and application types of 4 kinds of quantitative sales forecast methods, including time series method, regression analysis method, grey prediction model and artificial neural network, illustrates and sums up some combined algorithms of machine learning. The results show that the time series method is suitable for short-and medium-term forecast of clothing sales with small discrete degree of historical data and few influence factors; in the regression analysis, multiple regression method is more suitable for the forecast of clothing sales with multiple influence factors than single regression method in computational theory; grey prediction model is suitable for the forecast of clothing sales with smooth data and few influence factors, while the artificial neural network is suitable for the forecast of sales of fashionable garments with highly discrete sales data.

Key words: forecast of clothing sales, forecast method, time series method, regression analysis method, grey model, artificial neural network

摘要:

服装销售预测是服装企业商品企划中必不可少的环节之一。为有效帮助服装商品企划人员及相关学者根据实际情况快速选择合适的服装销售预测方法,对时间序列法、回归分析法、灰色预测模型及人工神经网络4类定量销售预测方法从优缺点、优化历程及适用类型3个方面进行梳理总结,并对机器学习的部分组合算法进行举例与归纳。分析得出:时间序列法适用于历史数据离散程度小且影响因素少的短中期服装销售预测;回归分析法中多元回归法比一元回归法在算理上更适合具有多因素影响的服装销售预测;灰色预测模型适用于数据平滑且影响因素较少的服装销售预测;人工神经网络则适合销售数据离散程度大的时尚型服装销售预测。

关键词: 服装销售预测, 预测方法, 时间序列法, 回归分析法, 灰色模型, 人工神经网络

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