A Novel Framework for Online Sales Burst Prediction

  • Rui Chen
  • Jiajun Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10536)


With the rapid growth of e-commerce, a large number of online transactions are processed every day. In this paper, we take the initiative to conduct a systematic study of the challenging prediction problems of sales bursts. Here, we propose a novel model to detect bursts, find the bursty features, namely the start time of the burst, the peak value of the burst and the off-burst value, and predict the entire burst shape. Our model analyzes the features of similar sales bursts in the same category, and applies them to generate the prediction. We argue that the framework is capable of capturing the seasonal and categorical features of sales burst. Based on the real data from, we conduct extensive experiments and discover that the proposed model makes a relative MSE improvement of 71% and 30% over LSTM and ARMA.


Burst prediction E-commerce 



This work was supported by the National Natural Science Foundation of China (No. 61602487), the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (No. 2015030275).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Big Data Analytics and Intelligence Lab, School of InformationRenmin University of ChinaBeijingChina

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