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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 831201, 15 pages
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- G. B. Huang and L. Chen, “Convex incremental extreme learning machine,” Neurocomputing, vol. 70, no. 16–18, pp. 3056–3062, 2007.
- G. B. Huang and L. Chen, “Enhanced random search based incremental extreme learning machine,” Neurocomputing, vol. 71, no. 16–18, pp. 3460–3468, 2008.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 2000.
- 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.
- 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.
- 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.
- 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.
- S. Haykin, Neural Network: A Comprehensive Foundation, Prentice Hall, New Jersey, NJ, USA, 1999.
- Y. Chauvin and D. E. Rumelhart, Backpropagation: Theory, Architectures, and Applications, Lawrence Erlbaum Associates, New Jersey, NJ, USA, 1995.