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The Scientific World Journal
Volume 2014, Article ID 624017, 8 pages
http://dx.doi.org/10.1155/2014/624017
Research Article

A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression

1Department of Industrial Management, Chien Hsin University of Science and Technology, Taoyuan County 32097, Taiwan
2School of Medical Informatics, Chung Shan Medical University, Information Technology Office, Chung Shan Medical University Hospital, Taichung City 40201, Taiwan

Received 22 April 2014; Accepted 5 June 2014; Published 17 June 2014

Academic Editor: Kuo-Nan Huang

Copyright © 2014 Chi-Jie Lu and Chi-Chang Chang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.