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Time series online prediction algorithm based on least squares support vector machine

  • Wu Qiong  (吴琼)Email author
  • Liu Wen-ying  (刘文颖)
  • Yang Yi-han  (杨以涵)
Article

Abstract

Deficiencies of applying the traditional least squares support vector machine (LS-SVM) to time series online prediction were specified. According to the kernel function matrix’s property and using the recursive calculation of block matrix, a new time series online prediction algorithm based on improved LS-SVM was proposed. The historical training results were fully utilized and the computing speed of LS-SVM was enhanced. Then, the improved algorithm was applied to time series online prediction. Based on the operational data provided by the Northwest Power Grid of China, the method was used in the transient stability prediction of electric power system. The results show that, compared with the calculation time of the traditional LS-SVM(75-1 600 ms), that of the proposed method in different time windows is 40–60 ms, and the prediction accuracy(normalized root mean squared error) of the proposed method is above 0.8. So the improved method is better than the traditional LS-SVM and more suitable for time series online prediction.

Key words

time series prediction machine learning support vector machine statistical learning theory 

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References

  1. [1]
    KUGIUMTZIS D, LINGIARDE O C, CHRISTOPH-ERSEN N. Regularized local linear prediction of chaotic time series[J]. Physica D, 1998, 11(2): 344–360.MathSciNetCrossRefGoogle Scholar
  2. [2]
    WHITEHEAD B A, CHOATE T D. Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction[J]. IEEE Transactions on Neural Networks, 1996, 7(4): 869–880.CrossRefGoogle Scholar
  3. [3]
    YEE P, HAYKIN S. A dynamic regularized radial basis function network for nonlinear, nonstationary time series prediction[J]. IEEE Transactions on Signal Processing, 1999, 47(9): 2503–2521.MathSciNetCrossRefGoogle Scholar
  4. [4]
    GENCAY R, LIU T. Nonlinear modeling and prediction with feedforward and recurrent networks[J]. Physica D, 1997, 10(8): 119–134.CrossRefGoogle Scholar
  5. [5]
    YU Li-xin, ZHANG Yan-qing. Evolutionary fuzzy neural networks for hybrid financial prediction[J]. IEEE Transactions on Systems, Man and Cybernetics, Part C, 2005, 35(2): 244–249.CrossRefGoogle Scholar
  6. [6]
    GHAZALI R, HUSSAIN A, EL-DEREDY W. Application of ridge polynomial neural networks to financial time series prediction[C]// International Joint Conference on Neural Networks. Vancouver, BC, Canada: Tyndale House Press, 2006: 235–239.Google Scholar
  7. [7]
    VAPNIK V N. The Nature of Statistical Learning Theory[M]. New York: Spring-Verlag Press, 1995.CrossRefGoogle Scholar
  8. [8]
    CHERKASSKY V, MULIER F. Learning from Data-Concepts: Theory and Methods[M]. New York: John Wiley Sons Press, 1998.zbMATHGoogle Scholar
  9. [9]
    JOACHIMS T. Text categorization with support vector machines: learning with many relevant features[C]// Proceedings of the European Conference on Machine Learning(ECML). Paris: John Wiley Sons Publisher, 1998: 137–142.Google Scholar
  10. [10]
    GUYON I, WESTON J, BARNHILL S. Gene selection for cancer classification using support vector machines[J]. Machine Learning, 2002, 46(1): 389–422.CrossRefGoogle Scholar
  11. [11]
    HE Xue-wen, ZHAO Hai-ming. Support vector machine and its application to machinery fault diagnosis[J]. Journal of Central South University of Technology: Natural Science, 2005, 36(1): 97–101. (in Chinese)MathSciNetGoogle Scholar
  12. [12]
    ZHONG Wei-min, PI Dao-ying, SUN You-xian. Support vector machine based nonlinear model multi-step-ahead optimizing predictive control[J]. Journal of Central South University of Technology, 2005, 12(5): 591–595.CrossRefGoogle Scholar
  13. [13]
    KIVINEN J, SMOLA A, WILLIAMSON R. Online learning with kernels: Advances in Neural Information Processing Systems[M]. Cambridge, MA: MIT Press, 2002.Google Scholar
  14. [14]
    RALAIVOLA L. Incremental support vector machine learning: A local approach[C]// Proceedings of the International on Conference on Artificial Neural Networks. Vienna: St Martin Publisher, 2001: 322–329.Google Scholar
  15. [15]
    KUH A. Adaptive kernel methods for CDMA systems[C]// Proceedings of the International Joint Conference on Neural networks. Washington DC: Spring-Verlag Publisher, 2001: 1404–1409.Google Scholar
  16. [16]
    SUYKENS J, VANDEWALE J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293–300.CrossRefGoogle Scholar

Copyright information

© Published by: Central South University Press, Sole distributor outside Mainland China: Springer 2007

Authors and Affiliations

  • Wu Qiong  (吴琼)
    • 1
    Email author
  • Liu Wen-ying  (刘文颖)
    • 1
  • Yang Yi-han  (杨以涵)
    • 1
  1. 1.Key Laboratory of Power System Protection and Dynamic Security Monitory and Control of Ministry of EducationNorth China Electric Power UniversityBeijingChina

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