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The Scientific World Journal
Volume 2014, Article ID 438132, 9 pages
http://dx.doi.org/10.1155/2014/438132
Research Article

Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting

1International Monetary Institute, Financial School, Renmin University of China, Beijing 100872, China
2Department of Business Administration, Lunghwa University of Science and Technology, Taoyuan County 33306, Taiwan
3Department of Industrial Management, Chien Hsin University of Science and Technology, Taoyuan County 32097, Taiwan

Received 4 April 2014; Revised 14 May 2014; Accepted 21 May 2014; Published 5 June 2014

Academic Editor: Chih-Chou Chiu

Copyright © 2014 Wensheng Dai et al. 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 is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.