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

  • Zengwei Zheng
  • Junjie Du
  • Yanzhen Zhou
  • Lin SunEmail author
  • Meimei Huo
  • Jianzhong Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)

Abstract

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.

Keywords

Consumer traffic forecasting Multiple factor Sparse regression 

Notes

Acknowledgment

This work is supported by Zhejiang Provincial Natural Science Foundation of China (No. LY17F020008).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zengwei Zheng
    • 1
  • Junjie Du
    • 1
    • 2
  • Yanzhen Zhou
    • 1
    • 2
  • Lin Sun
    • 1
    Email author
  • Meimei Huo
    • 1
  • Jianzhong Wu
    • 1
  1. 1.Intelligent Plant Factory of Zhejiang Province Engineering LabZhejiang University City CollegeHangzhouChina
  2. 2.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina

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