服装销售定量预测方法研究进展

doi:10.19398/j.att.202104036

摘要/Abstract

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

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

中图分类号:

TN941.26

郑金峰, 罗戎蕾. 服装销售定量预测方法研究进展[J]. 现代纺织技术, 2022, 30(2): 27-35.

ZHENG Jinfeng, LUO Ronglei. Research progress on quantitative forecast methods of clothing sales[J]. Advanced Textile Technology, 2022, 30(2): 27-35.

图/表 2

参考文献 54

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主要方法	研究学者	优缺点	适用类型
时间序列法(TSPM)	Giri等^[6];李欣炳^[7];Xu等^[8];陈银光等^[9];Thomassey等^[10];Issao等^[11];宋宪捷等^[12];Doszyn等^[13]	简单易掌握、运算量小、计算速度快、精确度较高;缺点是不能准确分析事物发展的内在规律。	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。(基础型)
回归分析法 (RAM)	Nivasanon等^[14];Demiriz等^[15];李锋等^[16];池可等^[17];Tehrani等^[18]	优点是简单易计算,适用于多变量预测,可计量各因素之间的相关程度与拟合程度;缺点是易出现过拟合。	不适合长期预测;SRA适用于时尚型服装销量预测;MRA则适用于季节型服装销售预测。
灰色预测模型(GM)	孙利辉^[19];江玉杰^[20];Xia等^[21];韩曙光等^[22];王昕彤等^[23,24];黄鸿云等^[25];徐倩茹^[26];刘思峰等^[27];刘卫校^[28,29]	优点是计算量小,不需要大量数据,适用于短中期预测;缺点是原始数据需符合残差检验或经处理后符合残差检验。	适合历史数据平滑且影响因素小的中短期销售预测。在算理上,GM(1,N)比GM(1,1)更适合具有多因素影响的服装销售预测。
人工神经网络(ANN)	罗戎蕾等^[30];圣文顺等^[31];喻宝禄等^[32];汪芸芳等^[33];Wong等^[34];于淼^[35];Sun等^[36];池可^[37]	优点是可以充分逼近任意非线性关系,有自学习能力及高速寻找最优解的能力;缺点是训练时间长、可视性差、需要大量数据。	适合历史数据离散程度大且影响因素大的中短期服装销售预测。(时尚型)
机器学习组合算法	Cui等^[38];Teucke等^[39];Singh等^[40];Ferreira等^[41];赵学斌等^[42];张小娟^[43];Huang等^[44];Aksoy等^[45];Au等^[46]	—	ANFIS适用于数据非线性或缺失的短期销售预测;ENN适用于历史数据波动小的预测;SARIMA适用于历史数据CVS值大的预测。非线性模型(SVM、梯度提升模型、随机森林)的销售预测性能优于线性模型。

主要方法	研究学者	优缺点	适用类型
时间序列法(TSPM)	Giri等^[6];李欣炳^[7];Xu等^[8];陈银光等^[9];Thomassey等^[10];Issao等^[11];宋宪捷等^[12];Doszyn等^[13]	简单易掌握、运算量小、计算速度快、精确度较高;缺点是不能准确分析事物发展的内在规律。	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。(基础型)
回归分析法 (RAM)	Nivasanon等^[14];Demiriz等^[15];李锋等^[16];池可等^[17];Tehrani等^[18]	优点是简单易计算,适用于多变量预测,可计量各因素之间的相关程度与拟合程度;缺点是易出现过拟合。	不适合长期预测;SRA适用于时尚型服装销量预测;MRA则适用于季节型服装销售预测。
灰色预测模型(GM)	孙利辉^[19];江玉杰^[20];Xia等^[21];韩曙光等^[22];王昕彤等^[23,24];黄鸿云等^[25];徐倩茹^[26];刘思峰等^[27];刘卫校^[28,29]	优点是计算量小,不需要大量数据,适用于短中期预测;缺点是原始数据需符合残差检验或经处理后符合残差检验。	适合历史数据平滑且影响因素小的中短期销售预测。在算理上,GM(1,N)比GM(1,1)更适合具有多因素影响的服装销售预测。
人工神经网络(ANN)	罗戎蕾等^[30];圣文顺等^[31];喻宝禄等^[32];汪芸芳等^[33];Wong等^[34];于淼^[35];Sun等^[36];池可^[37]	优点是可以充分逼近任意非线性关系,有自学习能力及高速寻找最优解的能力;缺点是训练时间长、可视性差、需要大量数据。	适合历史数据离散程度大且影响因素大的中短期服装销售预测。(时尚型)
机器学习组合算法	Cui等^[38];Teucke等^[39];Singh等^[40];Ferreira等^[41];赵学斌等^[42];张小娟^[43];Huang等^[44];Aksoy等^[45];Au等^[46]	—	ANFIS适用于数据非线性或缺失的短期销售预测;ENN适用于历史数据波动小的预测;SARIMA适用于历史数据CVS值大的预测。非线性模型(SVM、梯度提升模型、随机森林)的销售预测性能优于线性模型。