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Mathematical Problems in Engineering
Volume 2015, Article ID 484093, 13 pages
http://dx.doi.org/10.1155/2015/484093
Research Article

Online Sequential Prediction for Nonstationary Time Series with New Weight-Setting Strategy Using Extreme Learning Machine

1College of Computer and Information Engineering, Henan Normal University, Henan, Xinxiang 453007, China
2Management Institute, Xinxiang Medical University, Henan, Xinxiang 453003, China

Received 21 August 2014; Accepted 12 October 2014

Academic Editor: Amaury Lendasse

Copyright © 2015 Wentao Mao 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.

Linked References

  1. J. G. de Gooijer and R. J. Hyndman, “25 years of time series forecasting,” International Journal of Forecasting, vol. 22, no. 3, pp. 443–473, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. F. Takens, “Detecting strange attractors in turbulence,” in Dynamical Systems and Turbulence, Warwick, vol. 1981, pp. 366–381, 1980. View at Google Scholar
  3. S. Mukherjee, E. Osuna, and F. Girosi, “Nonlinear prediction of chaotic time series using support vector machines,” in Proceedings of the 7th IEEE Workshop on Neural Networks for Signal Processing (NNSP '97), pp. 511–520, IEEE Press, Amelia Island, Fla, USA, September 1997. View at Scopus
  4. K. Kim, “Financial time series forecasting using support vector machines,” Neurocomputing, vol. 55, no. 1-2, pp. 307–319, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. D. Du, X. Li, M. Fei, and G. W. Irwin, “A novel locally regularized automatic construction method for RBF neural models,” Neurocomputing, vol. 98, pp. 4–11, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. P. Yee and S. Haykin, “A dynamic regularized radial basis function network for nonlinear, nonstationary time series prediction,” IEEE Transactions on Signal Processing, vol. 47, no. 9, pp. 2503–2521, 1999. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  7. J. W. Cao, T. Chen, and J. Fan, “Fast online learning algorithm for landmark recognition based on BoW framework,” in Proceedings of the 9th IEEE Conference on Industrial Electronics and Applications, pp. 1163–1168, Hangzhou, China, 2014.
  8. F. Corona and A. Lendasse, “Variable scaling for time series prediction,” in Proceedings of European Symposium on Time Series Prediction (ESTSP '07), pp. 69–76, Espoo, Finland, 2007.
  9. 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
  10. 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, Part B: Cybernetics, vol. 42, no. 2, pp. 513–529, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. J. W. Cao, Z. Lin, G. B. Huang, and N. Liu, “Voting based extreme learning machine,” Information Sciences, vol. 185, pp. 66–77, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. N.-Y. Liang, G.-B. Huang, P. Saratchandran, and N. Sundararajan, “A fast and accurate online sequential learning algorithm for feedforward networks,” IEEE Transactions on Neural Networks, vol. 17, no. 6, pp. 1411–1423, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. C.-F. Lin and S.-D. Wang, “Fuzzy support vector machines,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 464–471, 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. F. E. H. Tay and L. J. Cao, “Modified support vector machines in financial time series forecasting,” Neurocomputing, vol. 48, pp. 847–861, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  15. W. Mao, G. Yan, and L. Dong, “Weighted solution path algorithm of support vector regression based on heuristic weight-setting optimization,” Neurocomputing, vol. 73, no. 1–3, pp. 495–505, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. Bao, T. Xiong, and Z. Hu, “Multi-step-ahead time series prediction using multiple-output support vector regression,” Neurocomputing, vol. 129, pp. 482–493, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. X. Wang and M. Han, “Online sequential extreme learning machine with kernels for nonstationary time series prediction,” Neurocomputing, vol. 145, pp. 90–97, 2014. View at Publisher · View at Google Scholar
  18. A. Grigorievskiy, Y. Miche, A.-M. Ventelä, E. Séverin, and A. Lendasse, “Long-term time series prediction using OP-ELM,” Neural Networks, vol. 51, pp. 50–56, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Rong, G.-B. Huang, N. Sundarajan et al., “Online sequential fuzzy extreme learning machine for function approximation and classification problems,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybemetics, vol. 39, no. 4, pp. 1067–1072, 2009. View at Google Scholar
  20. D. M. Allen, “The relationship between variable selection and data augmentation and a method for prediction,” Technometrics, vol. 16, pp. 125–127, 1974. View at Google Scholar · View at MathSciNet · View at Scopus
  21. Y. Miche, M. van Heeswijk, P. Bas, O. Simula, and A. Lendasse, “TROP-ELM: a double-regularized ELM using LARS and Tikhonov regularization,” Neurocomputing, vol. 74, no. 16, pp. 2413–2421, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. G.-B. Huang and H. A. Babri, “Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions,” IEEE Transactions on Neural Networks, vol. 9, no. 1, pp. 224–229, 1998. View at Publisher · View at Google Scholar · View at Scopus
  23. Y. Lan, Y. C. Soh, and G. B. Huang, “Two-stage extreme learning machine for regression,” Neurocomputing, vol. 73, no. 16-18, pp. 3028–3038, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. G. Feng, G.-B. Huang, Q. Lin, and R. Gay, “Error minimized extreme learning machine with growth of hidden nodes and incremental learning,” IEEE Transactions on Neural Networks, vol. 20, no. 8, pp. 1352–1357, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. X.-Y. Liu, P. Li, and C.-H. Gao, “Fast leave-one-out cross-validation algorithm for extreme learning machine,” Journal of Shanghai Jiaotong University, vol. 45, no. 8, pp. 6–11, 2011. View at Google Scholar · View at Scopus
  26. X. Zhang and H.-L. Wang, “Local extreme learning machine and its application to condition on-line monitoring,” Journal of Shanghai Jiaotong University, vol. 45, no. 2, pp. 236–240, 2011. View at Google Scholar · View at Scopus
  27. C.-M. Vong, W.-F. Ip, P.-K. Wong, and C.-C. Chiu, “Predicting minority class for suspended particulate matters level by extreme learning machine,” Neurocomputing, vol. 128, pp. 136–144, 2014. View at Publisher · View at Google Scholar · View at Scopus