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How Much Can A Retailer Sell? Sales Forecasting on Tmall

  • Chaochao ChenEmail author
  • Ziqi Liu
  • Jun Zhou
  • Xiaolong Li
  • Yuan Qi
  • Yujing Jiao
  • Xingyu Zhong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11440)

Abstract

Time-series forecasting is an important task in both academic and industry, which can be applied to solve many real forecasting problems like stock, water-supply, and sales predictions. In this paper, we study the case of retailers’ sales forecasting on Tmall—the world’s leading online B2C platform. By analyzing the data, we have two main observations, i.e., sales seasonality after we group different groups of retails and a Tweedie distribution after we transform the sales (target to forecast). Based on our observations, we design two mechanisms for sales forecasting, i.e., seasonality extraction and distribution transformation. First, we adopt Fourier decomposition to automatically extract the seasonalities for different categories of retailers, which can further be used as additional features for any established regression algorithms. Second, we propose to optimize the Tweedie loss of sales after logarithmic transformations. We apply these two mechanisms to classic regression models, i.e., neural network and Gradient Boosting Decision Tree, and the experimental results on Tmall dataset show that both mechanisms can significantly improve the forecasting results.

Keywords

Sales forecasting Tweedie distribution Distribution transform Seasonality extraction 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chaochao Chen
    • 1
    Email author
  • Ziqi Liu
    • 1
  • Jun Zhou
    • 1
  • Xiaolong Li
    • 1
  • Yuan Qi
    • 1
  • Yujing Jiao
    • 1
  • Xingyu Zhong
    • 1
  1. 1.Ant Financial Services GroupHangzhouChina

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