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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 985930, 12 pages
Time Series Adaptive Online Prediction Method Combined with Modified LS-SVR and AGO
1School of Computer Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
2School of Software and Microelectronics, Northwestern Polytechnical University, Xi’an 710072, China
3Science and Technology Commission, Aviation Industry Corporation of China, Beijing 100068, China
Received 27 July 2012; Revised 19 November 2012; Accepted 29 November 2012
Academic Editor: Huaguang Zhang
Copyright © 2012 Guo Yangming 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.
- W. Caesarendra, A. Widodo, P. H. Thom, B. S. Yang, and J. D. Setiawan, “Combined probability approach and indirect data-driven method for bearing degradation prognostics,” IEEE Transactions on Reliability, vol. 60, no. 1, pp. 14–20, 2011.
- D. Liu, Y. Peng, and X. Y. Peng, “Online adaptive status prediction strategy for data-driven fault prognostics of complex systems,” in Proceedings of the Prognostics and System Health Management Conference (PHM '11), pp. 1–6, Shenzhen, China, May 2011.
- M. Pecht and R. Jaai, “A prognostics and health management roadmap for information and electronics-rich systems,” IEICE Fundamentals Review, vol. 3, no. 4, pp. 25–32, 2010.
- J. Qu and M. J. Zuo, “An LSSVR-based algorithm for online system condition prognostics,” Expert Systems with Applications, vol. 39, no. 5, pp. 6089–6102, 2012.
- M. Qi and G. P. Zhang, “Trend time-series modeling and forecasting with neural networks,” IEEE Transactions on Neural Networks, vol. 19, no. 5, pp. 808–816, 2008.
- V. V. Gavrishchaka and S. Banerjee, “Support vector machine as an efficient framework for stock market volatility forecasting,” Computational Management Science, vol. 3, no. 2, pp. 147–160, 2006.
- Y. M. Guo, C. B. Ran, X. L. Li, and J. Z. Ma, “Adaptive online prediction method based on LS-SVR and its application in an electronic system,” Journal of Zhejiang University Science C, vol. 13, no. 12, pp. 881–890, 2012.
- V. Kecman, Learning and Soft Computing: Support Vector Machines. Neural Networks, and Fuzzy Logic Models, MIT Press, Cambridge, Mass, USA, 2001.
- J. V. Hansen and R. D. Nelson, “Neural networks and traditional time series methods: a synergistic combination in state economic forecasts,” IEEE Transactions on Neural Networks, vol. 8, no. 4, pp. 863–873, 1997.
- V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.
- J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewaller, Least Squares Support Vector Machines, World Scientific Publishing, 2002.
- G. Hebin and G. Xiaoqing, “Application of least squares support vector regression in network flow forecasting,” in Proceedings of the 2nd International Conference on Computer Engineering and Technology (ICCET '10), pp. V7-342–V7-345, April 2010.
- Y. H. Zhao, P. Zhong, and K. N. Wang, “Application of least squares support vector regression based on time series in prediction of gas,” Journal of Convergence Information Technology, vol. 6, no. 1, pp. 243–250, 2011.
- Y. Guo, Z. Zhai, and H. Jiang, “Weighted prediction of multi-parameter chaotic time series using least squares support vector regression (LS-SVR),” Journal of Northwestern Polytechnical University, vol. 27, no. 1, pp. 83–87, 2009.
- A. Shilton, D. T. H. Lai, and M. Palaniswami, “A division algebraic framework for multidimensional support vector regression,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 40, no. 2, pp. 517–528, 2010.
- Y. M. Guo, X. L. Li, G. H. Bai, and J. Z. Ma, “Time series prediction method based on LS-SVR with modified Gaussian RBF,” in Neural Information Processing, vol. 7664 of Lecture Notes in Computer Science, Part 2, pp. 9–17, 2012.
- H. Liu and Z. H. Sun, “An online algorithm for support vector machine based on subgradient projection,” Control Engineering and Applied Informatics, vol. 13, no. 3, pp. 18–24, 2011.
- S. Shalev-Shwartz, Online learning: theory, algorithms, and applications [Ph.D. thesis], The Senate of the Hebrew University, July 2007.
- W. M. Tang, “New forecasting model based on grey support vector machine,” Journal of Systems Engineering, vol. 21, no. 4, pp. 410–413, 2006.
- D. H. Zhan, Y. X. Bi, et al., “Power load forecasting method based on series grey neural network,” System Engineering-Theory & Practice, no. 23, pp. 128–132, 2004.
- J. L. Deng, The Primary Methods of Grey System Theory, Huazhong University of Science and Technology Press, Wuhan, China, 2004.
- F. Ojeda, J. A. K. Suykens, and B. De Moor, “Low rank updated LS-SVM classifiers for fast variable selection,” Neural Networks, vol. 21, no. 2-3, pp. 437–449, 2008.
- E. Yaakov, M. Shie, and M. Ron, “Sparse online greedy support vector regression,” in Proceedings of the 13th European Conference on Machine Learning, Berlin, Germany, 2002.
- J. K. Zhang, Linear Model’s Parameters Estimation Method and Its Improvement, National University of Defense Technology Press, Changsha, China, 1992.
- C. C. Cowen, Linear Algebra for Engineering and Science, Indiana West Pickle Press, West Lafayette, Ind, USA, 1996.
- M. Y. Ye, X. D. Wang, and H. R. Zhang, “Chaotic time series forecasting using online least squares support vector machine regression,” Acta Physica Sinica, vol. 54, no. 6, pp. 2568–2573, 2005.