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Multi-step Time Series Forecasting Using Ridge Polynomial Neural Network with Error-Output Feedbacks

  • Waddah WaheebEmail author
  • Rozaida Ghazali
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 652)

Abstract

Time series forecasting gets much attention due to its impact on many practical applications. Higher-order neural network with recurrent feedback is a powerful technique which used successfully for forecasting. It maintains fast learning and the ability to learn the dynamics of the series over time. For that, in this paper, we propose a novel model, called Ridge Polynomial Neural Network with Error-Output Feedbacks (RPNN-EOF), which combines three powerful properties: higher order terms, output feedback and error feedback. The well-known Mackey–Glass time series is used to evaluate the forecasting capability of RPNN-EOF. Results show that the proposed RPNN-EOF provides better understanding for the Mackey–Glass time series with root mean square error equal to 0.00416. This error is smaller than other models in the literature. Therefore, we can conclude that the RPNN-EOF can be applied successfully for time series forecasting. Furthermore, the error-output feedbacks can be investigated and applied with different neural network models.

Keywords

Time series forecasting Ridge polynomial neural network with error-output feedbacks Higher order neural networks Recurrent neural networks Mackey–glass equation 

Notes

Acknowledgments

The authors would like to thank Universiti Tun Hussein Onn Malaysia (UTHM) and Ministry of Higher Education (MOHE) Malaysia for financially supporting this research under the Fundamental Research Grant Scheme (FRGS), Vote No. 1235.

References

  1. 1.
    Al-Jumeily, D., Ghazali, R., Hussain, A.: Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks. PLOS ONE (2014)Google Scholar
  2. 2.
    Haykin, S.S.: Neural Networks and Learning Machines. Prentice Hall, New Jersey (2009)Google Scholar
  3. 3.
    Ghazali, R., Hussain, A.J., Liatsis, P., Tawfik, H.: The application of ridge polynomial neural network to multi-step ahead financial time series prediction. Neural Comput. Appl. 17(3), 311–323 (2008)CrossRefGoogle Scholar
  4. 4.
    Yu, X., Tang, L., Chen, Q., Xu, C.: Monotonicity and convergence of asynchronous update gradient method for ridge polynomial neural network. Neurocomputing 129, 437–444 (2014)CrossRefGoogle Scholar
  5. 5.
    Shin, Y., Ghosh, J.: Ridge polynomial networks. IEEE T. Neural Networ. 6(3), 610–622 (1995)CrossRefGoogle Scholar
  6. 6.
    Ghazali, R., Hussain, A.J., Nawi, N.M., Mohamad, B.: Non-stationary and stationary prediction of financial time series using dynamic ridge polynomial neural network. Neurocomputing 72(10), 2359–2367 (2009)CrossRefGoogle Scholar
  7. 7.
    Waheeb, W., Ghazali, R., Herawan, T.: Time series forecasting using ridge polynomial neural network with error feedback. In: Proceedings of the Second International Conference on Soft Computing and Data Mining (SCDM-2016) (in press)Google Scholar
  8. 8.
    Samarasinghe, S.: Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition. CRC Press, New York (2006)CrossRefzbMATHGoogle Scholar
  9. 9.
    Ghazali, R., Hussain, A.J., Liatsis, P.: Dynamic ridge polynomial neural network: forecasting the univariate non-stationary and stationary trading signals. Expert Syst. Appl. 38(4), 3765–3776 (2011)CrossRefGoogle Scholar
  10. 10.
    Dhahri, H., Alimi, A.: Automatic selection for the beta basis function neural networks. In: Krasnogor, N., Nicosia, G., Pavone, M., Pelta, D. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2007). Studies in Computational Intelligence, vol. 129, pp. 461–474. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Chen, Y.M., Lin, C.T.: Dynamic parameter optimization of evolutionary computation for on-line prediction of time series with changing dynamics. Appl. Soft Comput. 7(4), 1170–1176 (2007)CrossRefGoogle Scholar
  12. 12.
    Wang, H., Gu, H.: Prediction of chaotic time series based on neural network with legendre polynomials. In: Yu, W., He, H., Zhang, N. (eds.) ISNN 2009, Part I. LNCS, vol. 5551, pp. 836–843. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Aizenberg, I., Luchetta, A., Manetti, S.: A modified learning algorithm for the multilayer neural network with multi-valued neurons based on the complex QR decomposition. Soft. Comput. 16(4), 563–575 (2012)CrossRefGoogle Scholar
  14. 14.
    Tan, J.Y., Bong, D.B., Rigit, A.R.: Time series prediction using backpropagation network optimized by hybrid K-means-greedy algorithm. Eng. Lett. 20(3), 203–210 (2012)Google Scholar
  15. 15.
    Dhahri, H., Alimi, A.M.: The modified differential evolution and the RBF (MDE-RBF) neural network for time series prediction. In: The 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 2938–2943. IEEE (2006)Google Scholar
  16. 16.
    Lin, C.J., Chen, C.H., Lin, C.T.: A hybrid of cooperative particle swarm optimization and cultural algorithm for neural fuzzy networks and its prediction applications. IEEE Trans. Syst. Man Cybernetics Part C (Applications and Reviews) 39(1), 55–68 (2009)CrossRefGoogle Scholar
  17. 17.
    Lin, C.J.: Wavelet neural networks with a hybrid learning approach. J. Inf. Sci. Eng. 22(6), 1367–1387 (2006)Google Scholar
  18. 18.
    Herrera, L.J., Pomares, H., Rojas, I., Guillén, A., González, J., Awad, M., Herrera, A.: Multigrid-based fuzzy systems for time series prediction: CATS competition. Neurocomputing. 70(13), 2410–2425 (2007)CrossRefGoogle Scholar
  19. 19.
    Shin, Y., Ghosh, J.: The Pi-sigma network: an efficient higher-order neural network for pattern classification and function approximation. In: IJCNN-91-Seattle International Joint Conference Neural Networks, vol. 1, pp. 13–18. IEEE (1991)Google Scholar
  20. 20.
    Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1(2), 270–280 (1989)CrossRefGoogle Scholar
  21. 21.
    Dash, P.K., Satpathy, H.P., Liew, A.C., Rahman, S.: A real-time short-term load forecasting system using functional link network. IEEE Trans. Power Syst. 12(2), 675–680 (1997)CrossRefGoogle Scholar
  22. 22.
    Mahmud, M.S., Meesad, P.: An innovative recurrent error-based neuro-fuzzy system with momentum for stock price prediction. Soft. Comput. 1–19 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaParit RajaMalaysia
  2. 2.Computer Science DepartmentHodeidah UniversityHodeidahYemen

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