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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
Shin, Y., Ghosh, J.: Ridge Polynomial Networks. IEEE Transactions on Neural Networks 6(3), 610–622 (1995)
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)
Tawfik, H., Liatsis, P.: Prediction of Non-linear Time-Series using Higher-Order Neural Networks. In: Proceeding IWSSIP’97 Conference, Poznan, Poland (1997)
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)
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)
Pao, Y.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, Reading (1989)
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)
Medsker, L.R., Jain, L.C.: Recurrent Neural Networks: Design and Applications. CRC Press LLC, Boca Raton (2000)
Williams, R.J., Zipser, D.: A Learning Algorithm for Continually Running Fully Recurrent Neural Networks. Neural Computation 1, 270–280 (1989)
Thomason, M.: The Practitioner Method and Tools. Journal of Computational Intelligence in Finance 7(3), 36–45 (1999)
Thomason, M.: The Practitioner Method and Tools. Journal of Computational Intelligence in Finance 7(4), 35–45 (1999)
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)
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)
Haykin, S.: Neural Networks. A comprehensive Foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Ghazali, R., Hussain, A.J., Al-Jumeily, D., Merabti, M. (2007). Dynamic Ridge Polynomial Neural Networks in Exchange Rates Time Series Forecasting. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-71629-7_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71590-0
Online ISBN: 978-3-540-71629-7
eBook Packages: Computer ScienceComputer Science (R0)