Retail Consumer Traffic Multiple Factors Analysis and Forecasting Model Based on Sparse Regression

• Published in: Green, Pervasive, and Cloud Computing pp. 489 - 494
• Main Authors: Zheng, Zengwei, Du, Junjie, Zhou, Yanzhen, Sun, Lin, Huo, Meimei, Wu, Jianzhong
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2019
• Series: Lecture Notes in Computer Science
• Subjects: Consumer traffic forecasting; Multiple factor; Sparse regression
• ISBN: 3030150925; 9783030150921
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-15093-8_36

The rapid development of O2O business has increased the competition among offline shops in China. Accurate prediction of the shop’s customer traffic can help the stores to change the strategy of sales timely and improve their competitiveness. Customer traffic forecast is more than a problem of time series. In fact, customer traffic for the next period is related to some external factors except for historical traffic. In this paper, the external factors affecting the customer traffic are analyzed using sparse coding, and we propose a sparse regression forecasting model with these external factors. The obtained results show that these external factors have varying degrees of impact on consumer traffic, and the prediction accuracy is significantly improved after considering these factors.