A Novel Trigger Model for Sales Prediction with Data Mining Techniques

Wenjie Huang; Qing Zhang; Wei Xu; Hongjiao Fu; Mingming Wang; Xun Liang

doi:10.5334/dsj-2015-015

A Novel Trigger Model for Sales Prediction with Data Mining Techniques

Authors

Wenjie Huang School of Information, Renmin University of China, Beijing
Qing Zhang School of Information, Renmin University of China, Beijing
Wei Xu School of Information, Renmin University of China, Beijing
Hongjiao Fu School of Information, Renmin University of China, Beijing
Mingming Wang School of Information, Renmin University of China, Beijing
Xun Liang School of Information, Renmin University of China, Beijing

DOI:

https://doi.org/10.5334/dsj-2015-015

Keywords:

Sales prediction, Trigger model, Data mining, E-commerce

Abstract

Previous research on sales prediction has always used a single prediction model. However, no single model can perform the best for all kinds of merchandise. Accurate prediction results for just one commodity are meaningless to sellers. A general prediction for all commodities is needed. This paper illustrates a novel trigger system that can match certain kinds of commodities with a prediction model to give better prediction results for different kinds of commodities. We find some related factors for classification. Several classical prediction models are included as basic models for classification. We compared the results of the trigger model with other single models. The results show that the accuracy of the trigger model is better than that of a single model. This has implications for business in that sellers can utilize the proposed system to effectively predict the sales of several commodities.

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Published

2015-05-22

Issue

Vol. 14 (2015)

Section

Proceedings Papers

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