Dynamic Model Selection Based on Demand Pattern Classification in Retail Sales Forecasting

Erjiang E (), Ming Yu, Xin Tian () and Ye Tao
Additional contact information
Erjiang E: School of Management, Guangxi Minzu University, Nanning 530006, China
Ming Yu: Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
Xin Tian: School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
Ye Tao: Beijing Haolinju CVS Co., Ltd., Beijing 100190, China

Mathematics, 2022, vol. 10, issue 17, 1-16

Abstract: Many forecasting techniques have been applied to sales forecasts in the retail industry. However, no one prediction model is applicable to all cases. For demand forecasting of the same item, the different results of prediction models often confuse retailers. For large retail companies with a wide variety of products, it is difficult to find a suitable prediction model for each item. This study aims to propose a dynamic model selection approach that combines individual selection and combination forecasts based on both the demand patterns and the out-of-sample performance for each item. Firstly, based on both metrics of the squared coefficient of variation (CV 2 ) and the average inter-demand interval (ADI), we divide the demand patterns of items into four types: smooth, intermittent, erratic, and lumpy. Secondly, we select nine classical forecasting methods in the M-Competitions to build a pool of models. Thirdly, we design two dynamic weighting strategies to determine the final prediction, namely DWS-A and DWS-B. Finally, we verify the effectiveness of this approach by using two large datasets from an offline retailer and an online retailer in China. The empirical results show that these two strategies can effectively improve the accuracy of demand forecasting. The DWS-A method is suitable for items with the demand patterns of intermittent and lumpy, while the DWS-B method is suitable for items with the demand patterns of smooth and erratic.

Keywords: sales forecasting; demand pattern; dynamic weighting; model selection; retail (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/17/3179/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/17/3179/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:17:p:3179-:d:905933

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().