Dynamic Ridge Polynomial Neural Networks in Exchange Rates Time Series Forecasting

  • Rozaida Ghazali
  • Abir Jaafar Hussain
  • Dhiya Al-Jumeily
  • Madjid Merabti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4432)

Abstract

This paper proposed a novel dynamic system which utilizes Ridge Polynomial Neural Networks for the prediction of the exchange rate time series. We performed a set of simulations covering three uni-variate exchange rate signals which are; the JP/EU, JP/UK, and JP/US time series. The forecasting performance of the novel Dynamic Ridge Polynomial Neural Network is compared with the performance of the Multilayer Perceptron and the feedforward Ridge Polynomial Neural Network. The simulation results indicated that the proposed network demonstrated advantages in capturing noisy movement in the exchange rate signals with a higher profit return.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chen, A.S., Leung, M.T.: Regression Neural Network for Error Correction in Foreign Exchange Forecasting and Trading. Computers & Operations Research 31, 1049–1068 (2004)MATHCrossRefGoogle Scholar
  2. 2.
    Shin, Y., Ghosh, J.: Ridge Polynomial Networks. IEEE Transactions on Neural Networks 6(3), 610–622 (1995)CrossRefGoogle Scholar
  3. 3.
    Karnavas, Y.L., Papadopoulos, D.P.: Excitation Control of a Synchronous Machine using Polynomial Neural Networks. Journal of Electrical Engineering 55(7-8), 169–179 (2004)Google Scholar
  4. 4.
    Tawfik, H., Liatsis, P.: Prediction of Non-linear Time-Series using Higher-Order Neural Networks. In: Proceeding IWSSIP’97 Conference, Poznan, Poland (1997)Google Scholar
  5. 5.
    Voutriaridis, C., Boutalis, Y.S., Mertzios, G.: Ridge Polynomial Networks in Pattern Recognition. In: EC-VIP-MC 2003, 4th EURASIP Conference focused on Video/Image Processing and Multimedia Communications, Croatia, pp. 519–524 (2003)Google Scholar
  6. 6.
    Shin, Y., Ghosh, J.: The Pi-Sigma Networks: An efficient Higher-Order Neural Network for Pattern Classification and Function Approximation. In: Proceedings of International Joint Conference on Neural Networks, Seattle, Washington, vol. 1, pp. 13–18 (1991)Google Scholar
  7. 7.
    Pao, Y.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  8. 8.
    Yumlu, S., Gurgen, F.S., Okay, N.: A Comparison of Global, Recurrent and Smoothed-Piecewise Neural Models for Istanbul Stock Exchange (ISE) Prediction. Pattern Recognition Letters 26, 2093–2103 (2005)CrossRefGoogle Scholar
  9. 9.
    Medsker, L.R., Jain, L.C.: Recurrent Neural Networks: Design and Applications. CRC Press LLC, Boca Raton (2000)Google Scholar
  10. 10.
    Williams, R.J., Zipser, D.: A Learning Algorithm for Continually Running Fully Recurrent Neural Networks. Neural Computation 1, 270–280 (1989)CrossRefGoogle Scholar
  11. 11.
    Thomason, M.: The Practitioner Method and Tools. Journal of Computational Intelligence in Finance 7(3), 36–45 (1999)Google Scholar
  12. 12.
    Thomason, M.: The Practitioner Method and Tools. Journal of Computational Intelligence in Finance 7(4), 35–45 (1999)MathSciNetGoogle Scholar
  13. 13.
    Cao, L.J., Francis, E.H.T.: Support Vector Machine with Adaptive Parameters in Financial Time Series Forecasting. IEEE Transactions on Neural Networks 14(6), 1506–1518 (2003)CrossRefGoogle Scholar
  14. 14.
    Dunis, C.L., Williams, M.: Modeling and Trading the UER/USD Exchange Rate: Do Neural Network Models Perform Better? Derivatives Use, Trading and Regulation 8(3), 211–239 (2002)Google Scholar
  15. 15.
    Haykin, S.: Neural Networks. A comprehensive Foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)MATHGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Rozaida Ghazali
    • 1
  • Abir Jaafar Hussain
    • 1
  • Dhiya Al-Jumeily
    • 1
  • Madjid Merabti
    • 1
  1. 1.School of Computing & Mathematical Sciences, Liverpool John Moores University, L3 3AF LiverpoolEngland

Personalised recommendations