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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 831201, 15 pages
Research Article

Forecasting Computer Products Sales by Integrating Ensemble Empirical Mode Decomposition and Extreme Learning Machine

1Department of Industrial Management, Chien Hsin University of Science and Technology, Taoyuan County 32097, Zhongli, Taiwan
2Department of Statistics and Information Science, Fu Jen Catholic University, Xinzhuang District, New Taipei City 24205, Taiwan

Received 30 August 2012; Revised 12 November 2012; Accepted 13 November 2012

Academic Editor: Zexuan Zhu

Copyright © 2012 Chi-Jie Lu and Yuehjen E. Shao. 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.


A hybrid forecasting model that integrates ensemble empirical model decomposition (EEMD), and extreme learning machine (ELM) for computer products sales is proposed. The EEMD is a new piece of signal processing technology. It is based on the local characteristic time scales of a signal and could decompose the complicated signal into intrinsic mode functions (IMFs). The ELM is a novel learning algorithm for single-hidden-layer feedforward networks. In our proposed approach, the initial task is to apply the EEMD method to decompose the original sales data into a number of IMFs. The hidden useful information of the original data could be discovered in those IMFs. The IMFs are then integrated with the ELM method to develop an effective forecasting model for computer products sales. Experimental results from three real computer products sales data, including hard disk, display card, and notebook, showed that the proposed hybrid sales forecasting method outperforms the four comparative models and is an effective alternative for forecasting sales of computer products.