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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 528678, 7 pages
Crude Oil Price Prediction Based on a Dynamic Correcting Support Vector Regression Machine
College of Information and Control Engineering, China University of Petroleum, Qingdao, Shandong 266580, China
Received 10 December 2012; Accepted 28 January 2013
Academic Editor: Fuding Xie
Copyright © 2013 Li Shu-rong and Ge Yu-lei. 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.
- B. Hunt, P. Isard, and D. Laxton, “The macroeconomic effects of higher oil prices,” IMF Working Paper No.wp/01/14, 2001.
- Y. Fan, K. Wang, Y. J. Zhang, et al., “International crude oil market analysis and price forecast in 2009,” Bulletin of Chinese Academy of Sciences, vol. 4, no. 1, pp. 42–45, 2009.
- C. Morana, “A semiparametric approach to short-term oil price forecasting,” Energy Economics, vol. 23, no. 3, pp. 325–338, 2001.
- S. Mirmirani and H. Cheng Li, “A comparison of VAR and neural networks with genetic algorithm in forecasting price of oil,” Advances in Econometrics, vol. 19, pp. 203–223, 2004.
- Z. J. Ding, Q. Min, and Y. Lin, “Application of ARIMA model in forecasting prude oil price,” Logistics Technology, vol. 27, no. 10, pp. 156–159, 2008.
- J. P. Liu, S. Lin, T. Guo, and H. Y. Chen, “Nonlinear time series forecasting model and its application for oil price forecasting,” Journal of Management Science, vol. 24, no. 6, pp. 104–112, 2011.
- S. Y. Wang, L. Yu, and K. K. Lai, “Crude oil price forecasting with TEI@ I methodology,” Journal of Systems Sciences and Complexity, vol. 18, no. 2, pp. 145–166, 2005.
- W. Xie, L. Yu, S. Xu, and S. Wang, “A new method for crude oil price forecasting based on support vector machines,” Lecture Notes in Computer Science, vol. 3994, pp. 444–451, 2006.
- R. A. N. Mohammad and A. G. Ehsan, “A hybrid artificial intelligence approach to monthly forecasting of crude oil price time series,” in The Proceedings of the 10th International Conference on Engineering Applications of Neural Networks, pp. 160–167, 2007.
- S. Guo and P. Lai, “The time series mixed model and its application in price prediction of international crude oil,” Journal of Nanjing University of Information Science & Technology, vol. 2, no. 3, pp. 280–283, 2010.
- Y. B. Hou, J. Y. Du, and M. Wang, Neural Networks, Xidian University Press, Xi’an, China, 2007.
- H. Zhu, L. Qu, and H. Zhang, “Face detection based on wavelet transform and support vector machine,” Journal of Xi'an Jiaotong University, vol. 36, no. 9, pp. 947–950, 2002.
- R. Feng, C. L. Song, Y. Z. Zhang, and H. H. Shao, “Comparative study of soft sensor models based on support vector machines and RBF neural networks,” Journal of Shanghai Jiaotong University, vol. 37, pp. 122–125, 2003.
- J. Tao and N. Wang, “DNA computing based RNA genetic algorithm with applications in parameter estimation of chemical engineering processes,” Computers & Chemical Engineering, vol. 31, no. 12, pp. 1602–1618, 2007.
- K. Wang and N. Wang, “A protein inspired RNA genetic algorithm for parameter estimation in hydrocracking of heavy oil,” Chemical Engineering Journal, vol. 167, no. 1, pp. 228–239, 2011.
- K. Wang and N. Wang, “A novel RNA genetic algorithm for parameter estimation of dynamic systems,” Chemical Engineering Research & Design, vol. 88, no. 11, pp. 1485–1493, 2010.
- D. Bratton and J. Kennedy, “Defining a standard for particle swarm optimization,” in Proceedings of the IEEE Swarm Intelligence Symposium (SIS '07), pp. 120–127, April 2007.
- N. Y. Deng and Y. J. Tian, A New Method of Data Mining and Germany: Support Vector Machines, Science Press, Beijing, China, 2004.
- U. Thissen, R. Van Brakel, A. P. De Weijer, W. J. Melssen, and L. M. C. Buydens, “Using support vector machines for time series prediction,” Chemometrics and Intelligent Laboratory Systems, vol. 69, no. 1-2, pp. 35–49, 2003.
- K. J. Kim, “Financial time series forecasting using support vector machines,” Neurocomputing, vol. 55, no. 1-2, pp. 307–319, 2003.
- Y. Shi and R. Eberhart, “Modified particle swarm optimizer,” in Proceedings of the IEEE Congress on Evolutionary Computation, pp. 519–523, 1998.
- D. P. Clark, Molecular Biology: Understanding the Genetic Revolution, Academic Press, New York, NY, USA, 2005.
- J. Lis, “Genetic algorithm with the dynamic probability of mutation in the classification problem,” Pattern Recognition Letters, vol. 16, no. 12, pp. 1311–1320, 1995.
- M. Serpell and J. E. Smith, “Self-adaptation of mutation operator and probability for permutation representations in genetic algorithms,” Evolutionary Computation, vol. 18, no. 3, pp. 491–514, 2010.
- P. J. Angeline, “Evolutionary optimization versus PSO: philosophy and performance differences,” Evolutionary Programming, vol. 7, pp. 601–610, 1998.