Multiple SVMs enabled sales forecasting support system

Authors:
Yukun Bao;Zhitao Liu;Rui Zhang;Wei Huang
Affiliations:
Department of Management Science & Information System, School of Management, Huazhong University of Science and Technology, Wuhan, China;Department of Management Science & Information System, School of Management, Huazhong University of Science and Technology, Wuhan, China;Department of Management Science & Information System, School of Management, Huazhong University of Science and Technology, Wuhan, China;Department of Management Science & Information System, School of Management, Huazhong University of Science and Technology, Wuhan, China
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

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Abstract

This paper proposes a multiple SVMs enabled sales forecasting support system (SFSS). The SFSS has a two-stage system architecture. In the first stage, agglomerative hierarchical clustering(AHC) is used to partition the goods into several patterns based on similarity measure. In the second stage, multiple SVMs that best fit partitioned patterns are constructed by finding the appropriate kernel function and the optimal free parameters of SVMs. The experiment shows that this integrated system achieves significant improvement in forecasting performance compared with single SVMs models.