About this Journal Submit a Manuscript Table of Contents
Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 831201, 15 pages
http://dx.doi.org/10.1155/2012/831201
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.

Linked References

  1. C. J. Lu and Y. W. Wang, “Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting,” International Journal of Production Economics, vol. 128, no. 2, pp. 603–613, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Thomassey and M. Happiette, “A neural clustering and classification system for sales forecasting of new apparel items,” Applied Soft Computing Journal, vol. 7, no. 4, pp. 1177–1187, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. Z. L. Sun, T. M. Choi, K. F. Au, and Y. Yu, “Sales forecasting using extreme learning machine with applications in fashion retailing,” Decision Support Systems, vol. 46, no. 1, pp. 411–419, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. W. K. Wong and Z. X. Guo, “A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm,” International Journal of Production Economics, vol. 128, no. 2, pp. 614–624, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. P. C. Chang, C. H. Liu, and C. Y. Fan, “Data clustering and fuzzy neural network for sales forecasting: a case study in printed circuit board industry,” Knowledge-Based Systems, vol. 22, no. 5, pp. 344–355, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. C. J. Lu, T. S. Lee, and C. M. Lian, “Sales forecasting for computer wholesalers: a compcarison of multivariate adaptive regression splines and artificial neural networks,” Decision Support Systems, vol. 54, no. 1, pp. 584–596, 2012. View at Publisher · View at Google Scholar
  7. R. Fildes, K. Nikolopoulos, S. F. Crone, and A. A. Syntetos, “Forecasting and operational research: a review,” Journal of the Operational Research Society, vol. 59, no. 9, pp. 1150–1172, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  8. S. Thomassey and A. Fiordaliso, “A hybrid sales forecasting system based on clustering and decision trees,” Decision Support Systems, vol. 42, no. 1, pp. 408–421, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks,” in Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 985–990, Budapest, Hungary, July 2004. View at Scopus
  10. G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. G. B. Huang and L. Chen, “Convex incremental extreme learning machine,” Neurocomputing, vol. 70, no. 16–18, pp. 3056–3062, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. G. B. Huang and L. Chen, “Enhanced random search based incremental extreme learning machine,” Neurocomputing, vol. 71, no. 16–18, pp. 3460–3468, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. G. B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 42, pp. 513–529, 2012. View at Publisher · View at Google Scholar
  14. F. L. Chen and T. Y. Ou, “Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry,” Expert Systems with Applications, vol. 38, no. 3, pp. 1336–1345, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Xia, Y. Zhang, L. Weng, and X. Ye, “Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs,” Knowledge-Based Systems, vol. 36, pp. 253–259, 2012. View at Publisher · View at Google Scholar
  16. C. J. Lu, T. S. Lee, and C. C. Chiu, “Financial time series forecasting using independent component analysis and support vector regression,” Decision Support Systems, vol. 47, no. 2, pp. 115–125, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. K. L. Chen, C. C. Yeh, and T. L. Lu, “A hybrid demand forecasting model based on empirical mode decomposition and neural network in TFT-LCD industry,” Cybernetics and Systems, vol. 43, no. 5, pp. 426–441, 2012. View at Publisher · View at Google Scholar
  18. H. Liu and J. Wang, “Integrating independent component analysis and principal component analysis with neural network to predict Chinese Stock Market,” Mathematical Problems in Engineering, vol. 2011, Article ID 382659, 15 pages, 2011. View at Publisher · View at Google Scholar
  19. C. J. Lu, “Integrating independent component analysis-based denoising scheme with neural network for stock price prediction,” Expert Systems with Applications, vol. 37, no. 10, pp. 7056–7064, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society A, vol. 454, no. 1971, pp. 903–995, 1998. View at Publisher · View at Google Scholar · View at Scopus
  21. N. E. Huang, M. L. Wu, W. Qu, S. R. Long, and S. S. P. Shen, “Applications of Hilbert-Huang transform to non-stationary financial time series analysis,” Applied Stochastic Models in Business and Industry, vol. 19, no. 3, pp. 245–268, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  22. Z. Guo, W. Zhao, H. Lu, and J. Wang, “Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model,” Renewable Energy, vol. 37, no. 1, pp. 241–249, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. Y. Wei and M. C. Chen, “Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks,” Transportation Research Part C, vol. 21, no. 1, pp. 148–162, 2012. View at Publisher · View at Google Scholar
  24. Z. H. Wu and N. E. Huang, “Ensemble empirical mode decomposition: a noise assisted data analysis method,” Advances in Adaptive Data Analysis, vol. 1, no. 1, pp. 1–41, 2009. View at Publisher · View at Google Scholar
  25. L. Tang, L. Yu, S. Wang, J. Li, and S. Wang, “A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting,” Applied Energy, vol. 93, pp. 432–443, 2012. View at Publisher · View at Google Scholar
  26. R. Zhang, Y. Bao, and J. Zhang, “Forecasting erratic demand by support vector machines with ensemble empirical mode decomposition,” in Proceedings of the 3rd International Conference on Information Sciences and Interaction Sciences (ICIS '10), pp. 567–571, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 2000.
  28. G. Zhang, B. Eddy Patuwo, and M. Y. Hu, “Forecasting with artificial neural networks: the state of the art,” International Journal of Forecasting, vol. 14, no. 1, pp. 35–62, 1998. View at Publisher · View at Google Scholar · View at Scopus
  29. A. Vellido, P. J. G. Lisboa, and J. Vaughan, “Neural networks in business: a survey of applications (1992–1998),” Expert Systems with Applications, vol. 17, no. 1, pp. 51–70, 1999. View at Publisher · View at Google Scholar · View at Scopus
  30. V. Cherkassky and Y. Ma, “Practical selection of SVM parameters and noise estimation for SVM regression,” Neural Networks, vol. 17, no. 1, pp. 113–126, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  31. C. W. Hsu, C. C. Chang, and C. J. Lin, “A practical guide to support vector classification,” Tech. Rep., Department of Computer Science and Information Engineering, National Taiwan University, 2012.
  32. S. Haykin, Neural Network: A Comprehensive Foundation, Prentice Hall, New Jersey, NJ, USA, 1999.
  33. Y. Chauvin and D. E. Rumelhart, Backpropagation: Theory, Architectures, and Applications, Lawrence Erlbaum Associates, New Jersey, NJ, USA, 1995.