A Novel Framework for Online Sales Burst Prediction
Abstract
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 JD.com, we conduct extensive experiments and discover that the proposed model makes a relative MSE improvement of 71% and 30% over LSTM and ARMA.
Cite
Text
Chen and Liu. "A Novel Framework for Online Sales Burst Prediction." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71273-4_1Markdown
[Chen and Liu. "A Novel Framework for Online Sales Burst Prediction." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/chen2017ecmlpkdd-novel/) doi:10.1007/978-3-319-71273-4_1BibTeX
@inproceedings{chen2017ecmlpkdd-novel,
title = {{A Novel Framework for Online Sales Burst Prediction}},
author = {Chen, Rui and Liu, Jiajun},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2017},
pages = {3-14},
doi = {10.1007/978-3-319-71273-4_1},
url = {https://mlanthology.org/ecmlpkdd/2017/chen2017ecmlpkdd-novel/}
}