Research progress on quantitative forecast methods of clothing sales

doi:10.19398/j.att.202104036

Abstract

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

CLC Number:

TN941.26

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

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.

Figures/Tables 2

References 54

[1]	司守奎, 孙玺菁, 张德存. 数学建模算法与应用习题解答[M].2版. 北京: 国防工业出版社, 2015:397-425.
	SI Shoukui, SUN Xijing, ZHANG Decun. Mathematical Modeling Algorithm and Application[M]. 2nd Edition. Beijing: National Defense Industry Press, 2015: 397-425.
[2]	鞠雨宏, 张焕伟. 经济预测方法综述与选择[C]// 后疫情时代中国经济发展论坛论文集.长春:吉林省财政科学研究所, 2020: 10.
	JU Yuhong, ZHANG Huanwei. Summary and selection of economic forecasting methods[C]// Proceedings of the Forum on China's Economic Development in the Post-Epidemic Era. Changchun: Jilin Institute of financial Sciences, 2020: 10.
[3]	周小溪, 徐行, 孟剑飞, 等. 服装销售预测方法研究进展[J]. 针织工业, 2020(3):68-72.
	ZHOU Xiaoxi, XU Hang, MENG Jianfei, et al. Research progress of garment sales forecasting methods[J]. Knitting Industries. 2020(3):68-72.
[4]	李笑然, 姜毅. 销售预测中定量与定性分析方法的比较与探究[J]. 商业会计, 2017(6):65-67.
	LI Xiaoran, JIANG Yi. Comparison and exploration of quantitative and qualitative analysis methods in sales forecast[J]. Commercial Accounting, 2017(6):65-67.
[5]	梁炯聪. 基于时间序列的服装销售预测系统研究与应用[D]. 广州:广东工业大学, 2015.
	LIANG Jiongcong. Research and Application of Clothing Sales Forecasting System Based on Time Series[D]. Guangzhou: Guangdong University of Technology, 2015.
[6]	GIRI C, THOMASSEY S, ZENG X. Exploitation of social network data for forecasting garment sales[J]. International Journal of Computational Intelligence Systems, 2019, 12(2):1423-1435. DOI URL
[7]	李欣芮. 基于组合模型的销售量预测及优化[D]. 北京:华北电力大学, 2019.
	LI Xinrui. Sales Forecasting Based on Combination Model[D]. Beijing: North China Electric Power University, 2019.
[8]	XU W X, LIAO F J, LI S, et al. Design and application for the method of dynamic weighted moving average forecasting[C]// Sixth International Conference on Intelligent Systems Design and Engineering Applications. IEEE Computer Society, Guiyang, China. IEEE, 2015: 278-280.
[9]	陈银光, 于守健. 基于改进加权移动平均法的服装销售预测[J]. 智能计算机与应用, 2018, 8(6):54-56,60.
	CHEN Yinguang, YU Shoujian. Clothing sales forecast based on improved weighted moving average method[J]. Intelligent Computer and Application, 2018, 8(6):54-56, 60.
[10]	THOMASSEY S. Sales forecasts in clothing industry: The key success factor of the supply chain management[J]. International Journal of Production Economics, 2010, 128(2):470-483. DOI URL
[11]	ISSAO CHIBA G, MARIA MENDES LUNA M. Time series models: An analysis of demand forecast accuracy for a product line in a clothing company[J]. Revista Gestão da Produção Operações e Sistemas, 2020, 15(4):219-251. DOI URL
[12]	宋宪捷, 周建军, 鹿士凯, 等. 基于客流量的贝叶斯回归服装销售预测方法[J]. 丝绸, 2018, 55(4):44-48.
	SONG Xianjie, ZHOU Jianjun, LU Shikai, et al. Clothing sales forecast based on passenger flow and Bayesian regression model[J]. Journal of Silk, 2018, 55(4):44-48.
[13]	DOSZYN M. Intermittent demand forecasting in the enterprise: Empirical verification[J]. Journal of Forecasting, 2019, 38(5):459-469.
[14]	NIVASANON C, RUEKKASAEM L, AUNGKULA-NON P. Demand forecasting for online market stock[C]// IC4E' 19: Proceedings of the 10th International Conference on E-Education, E-Business, E-Management and E-Learning. Tokyo, Japan, New York: ACM Press, 2019: 292-297.
[15]	DEMIRIZ A. Demand Forecasting based on pairwise item associations[J]. Procedia Computer Science, 2014, 36:261-268. DOI URL
[16]	李锋, 袁琐云. 纺织印染品销售趋势分析系统的设计与实现[J]. 计算机应用, 2019, 39(S2):271-274.
	LI Feng, YUAN Suoyun. Design and implementation of textile printing and dyeing product sales trend analysis system[J]. Journal of Computer Applications, 2019, 39(S2):271-274.
[17]	池可, 陈雁. 基于对象信息的服装销售预测方法选择[J]. 纺织学报, 2009, 30(10):139-142.
	CHI Ke, CHEN Yan. Forecast method for garment sales based on object information[J]. Journal of Textile Research, 2009, 30(10):139-142.
[18]	TEHRANI A F, AHRENS D. Enhanced predictive models for purchasing in the fashion field by using kernel machine regression equipped with ordinal logistic regression[J]. Journal of Retailing and Consumer Services, 2016, 32:131-138. DOI URL
[19]	孙利辉. 基于灰色理论的服装销售预测及库存优化研究[D]. 杭州:浙江理工大学, 2016.
	SUN Lihui. Study on the Sales Forecasting of Clothing andInventory Problem Based on the Grey Theory[D]. Hangzhou: Zhejiang Sci-Tech University, 2016.
[20]	江玉杰. 基于新陈代谢GM(1,1)模型的服装产品销售预测[J]. 浙江纺织服装职业技术学院学报, 2017, 16(4):63-67.
	JIANG Yujie. Sales forecast of clothing products based on metabolic GM(1, 1) model[J]. Journal of Zhejiang Textile & Fashion College, 2017, 16(4):63-67.
[21]	XIA M, WONG WK. A seasonal discrete grey forecasting model for fashion retailing[J]. Knowledge-Based Systems, 2014, 57:119-126. DOI URL
[22]	韩曙光, 孙利辉. 基于改进GM(1,1)模型的服装配货决策研究[J]. 浙江理工大学学报(社会科学版), 2015(6):508-511.
	HAN Shuguang, SUN Lihui. Study on clothing distribution decisions based on improved GM(1, 1 model[J]. Journal of Zhejiang Sci-Tech University(Social Sciences Edition), 2015(6):508-511.
[23]	王昕彤, 王秀敏, 郭瑞良, 等. 基于灰色理论的服装企业销售预测模型[J]. 丝绸, 2020, 57(2):55-60.
	WANG Xintong, WANG Xiumin, GUO Ruiliang, et al. Sales prediction model of clothing enterprises based on grey theory[J]. Journal of Silk, 2020, 57(2):55-60.
[24]	王昕彤. 基于灰色理论的服装企业多渠道销售预测模型的研究[D]. 北京:北京服装学院, 2019.
	WANG Xintong. Research on the Prediction Modelof Clothing Enterprises Multichannel Sales Based on Grey Theory[D]. Beijing: Beijing Institute of Fashion Technology, 2019.
[25]	黄鸿云, 刘卫校, 丁佐华. 基于多维灰色模型及神经网络的销售预测[J]. 软件学报, 2019, 30(4):1031-1044.
	HUANG Hongyun, LIU Weixiao, DING Zuohua. Sales forecasting based on multi-dimensional grey model and neural network[J]. Journal of Software, 2019, 30(4):1031-1044.
[26]	徐倩茹. 基于组合预测模型的服装销量预测研究[D]. 西安:长安大学, 2018.
	XU Qianru. Research of the Forecast of Apparel Sales Based on the Combined Forecasting Model[D]. Xi'an: Chang'an University, 2018.
[27]	刘思峰, 曾波, 刘解放, 等. GM(1,1)模型的几种基本形式及其适用范围研究[J]. 系统工程与电子技术, 2014, 36(3):501-508.
	LIU Sifeng, ZENG Bo, LIU Jiefang, et al. Several basic models of GM(1, 1) and their applicable bound[J]. Systems Engineering and Electronics, 2014, 36(3):501-508.
[28]	刘卫校. 基于多维灰色模型与神经网络的销售预测模型研究[D]. 杭州:浙江理工大学, 2017.
	LIU Weixiao. The Research on Sales Forecasting Model Based on Multidimensional Grey Model and Neural Network[D]. Hangzhou: Zhejiang Sci-Tech University, 2017.
[29]	刘卫校. 基于离散灰色预测模型与人工神经网络混合智能模型的时尚销售预测[J]. 计算机应用, 2016, 36(12):3378-3384.
	LIU Weixiao. Hybrid intelligent model for fashion sales forecasting based ondiscrete grey forecasting model and artificial neural network[J]. Journal of Computer Applications, 2016, 36(12):3378-3384.
[30]	罗戎蕾, 刘绍华, 苏晨. 基于遗传算法的BP神经网络服装销售预测方法[J]. 北京邮电大学学报, 2014, 37(4):39-43.
	LUO Ronglei, LIU Shaohua, SU Chen. Garment sales forecast method based on geneticalgorithm and BP neural network[J]. Journal of Beijing University of Posts and Telecommunications, 2014, 37(4):39-43.
[31]	圣文顺, 赵翰驰, 孙艳文. 基于改进遗传算法优化BP神经网络的销售预测模型[J]. 计算机系统应用, 2019, 28(12):200-204.
	SHENG Wenshun, ZHAO Hanchi, SUN Yanwen. Sales forecasting model based on BP neural network optimized by improved genetic algorithms[J]. Computer Systems & Applications, 2019, 28(12):200-204.
[32]	喻宝禄, 段迅, 吴云. BP神经网络数据预测模型的建立及应用[J]. 计算机与数字工程, 2016, 44(3):482-486,545.
	YU Baolu, DUAN Xun, WU Yun. Establishment and application of data prediction model based on BP neural network[J]. Computer & Digital Engineering, 2016, 44(3):482-486, 545.
[33]	汪芸芳, 史意, 陈丽华. 基于BP神经网络及灰色GM(1,1)模型的服装供应链第三方库存预测应用研究[J]. 数学的实践与认识, 2020, 50(3):277-285.
	WANG Yunfang, SHI Yi, CHEN Lihua. Third-Party inventory forecasting application research of apparel supply chain based on BP neural network and grey GM(1, 1) model[J]. Mathematics in Practice and Theory, 2020, 50(3):277-285.
[34]	WONG W K, GUO Z X. A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm[J]. International Journal of Production Economics, 2010, 128(2):614-624. DOI URL
[35]	于淼. 基于机器学习理论的服装中期销售预测研究[D]. 杭州:浙江理工大学, 2015.
	YU Miao. Study on the Mid-term Sales Forecasting of Clothing Based on the Theory of Extreme Learning Machine[D]. Hangzhou: Zhejiang Sci-Tech Univer-sity, 2015.
[36]	SUN Z L, CHOI T, AU K, et al. Sales forecasting using extreme learning machine with applications in fashion retailing[J]. Decision Support Systems, 2008, 46(1):411-419. DOI URL
[37]	池可. 服装销售预测方法及RBF神经网络模型研究[D]. 苏州:苏州大学, 2009.
	CHI Ke. Research on the Methods and RBF Neural Network Model for Clothing Sale Prediction[D]. Suzhou: Soochow University, 2009.
[38]	CUI R, GALLINO S, MORENO A, et al. The operational value of social media information[J]. Production and Operations Management, 2018, 27(10):1749-1769. DOI URL
[39]	TEUCKE M, AIT-ALLA A, EL-BERISHY N, et al. Forecasting of seasonal apparel products[C]// 4th International Conference on Dynamics in Logistics. Cham: Springer International Publishing. 2015: 633-642.
[40]	SINGH PK, GUPTA Y, JHA N, et al. Fashion retail: Forecasting demand for new items[C]// KDD 2019 Workshop. Alaska-USA: Anchorage, 2019.
[41]	FERREIRAK J, LEE B H A, SIMCHI-LEVI D. Analytics for an online retailer: Demand forecasting and price optimization[J]. Manufacturing & Service Operations Management, 2016, 18(1):69-88.
[42]	赵学斌, 李大学, 谢名亮. 短生命周期产品的销量预测模型研究[J]. 计算机工程与设计, 2010, 31(11):2527-2530.
	ZHAO Xuebin, LI Daxue, XIE Mingliang. Study on sales forecasting models of short lifecycle products[J]. Computer Engineering and Design, 2010, 31(11):2527-2530.
[43]	张小娟. 自适应神经模糊推理系统(ANFIS)及其仿真[J]. 电子设计工程, 2012, 20(5):11-13.
	ZHANG Xiaojuan. Study on the adaptive network-based fuzzy inference system and simulation[J]. Electronic Design Engineering, 2012, 20(5):11-13.
[44]	HUANG H, LIU Q. Intelligent retail forecasting system for new clothing products considering stock-out[J]. Fibres and Textiles in Eastern Europe, 2017, 25(1):10-16.
[45]	AKSOY A, OZTURK N, SUCKY E. A decision support system for demand forecasting in the clothing industry[J]. International Journal of Clothing Science and Technology, 2012, 24(4):221-236. DOI URL
[46]	AU K, CHOI T, YU Y. Fashion retail forecasting by evolutionary neural networks[J]. International Journal of Production Economics, 2008, 114(2):615-630. DOI URL
[47]	FABOZZI F J, FOCARDI S M, RACHEV S T, et al. Autoregressive Moving Average Models[M]. New Jersey: John Wiley & Sons Inc, 2014:171-190.
[48]	李清茹, 吴俊, 叶冬青. 相关和回归分析的创始人:弗朗西斯·高尔顿[J]. 中华疾病控制杂志, 2018, 22(9):981-983.
	LI Qingru, WU Jun, YE Dongqing. The pioneer of correlation and regression analysis: Francis Galton[J]. Chinese Journal of Disease Control & Prevention, 2018, 22(9):981-983.
[49]	邓聚龙. 灰色控制系统[J]. 华中工学院学报, 1982, 10(3):9-18.
	DENG Julong. The grey control system[J]. Journal of Huazhong University of Science and Technology, 1982, 10(3):9-18.
[50]	周文浩, 曾波. 灰色关联度模型研究综述[J]. 统计与决策, 2020, 36(15):29-34.
	ZHOU Wenhao, ZENG Bo. A research review of grey relational degree model[J]. Statistics and Decision, 2020, 36(15):29-34.
[51]	张晶晶, 李泽霞. 灰色预测法在情报研究中的应用研究综述[J]. 图书情报工作, 2020, 64(24):106-114.
	ZHANG Jingjing, LI Zexia. Review on the application of grey prediction method in information research[J]. Library and Information Service, 2020, 64(24):106-114.
[52]	张驰, 郭媛, 黎明. 人工神经网络模型发展及应用综述[J]. 计算机工程与应用, 2021, 57(11):57-69.
	ZHANG Chi, GUO Yuan, LI Ming. Review of development and application of artificial neural network models[J]. Computer Engineering and Applications, 2021, 57(11):57-69.
[53]	杨剑锋, 乔佩蕊, 李永梅, 等. 机器学习分类问题及算法研究综述[J]. 统计与决策, 2019, 35(6):36-40.
	YANG Jianfeng, QIAO Peirui, LI Yongmei, et al. A review of machine-learning classification and algorithms[J]. Statistics and Decision, 2019, 35(6):36-40.
[54]	DIFRANCO G, SANTURRO M. Machine learning, artificial neural networks and social research[J]. Quality & Quantity: International Journal of Methodology, 2021, 55(3):1007-1025.

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。