Abstract
Predictive models are generally fitted directly from the original noisy data. It is well known that noise can seriously limit the prediction performance on time series. In this study, we apply the nonlinear noise reduction methods to the problem of foreign exchange rates forecasting with neural networks (NNs). The experiment results show that the nonlinear noise reduction methods can improve the prediction performance of NNs. Based on the modified Diebold-Mariano test, the improvement is not statistically significant in most cases. We may need more effective nonlinear noise reduction methods to improve prediction performance further. On the other hand, it indicates that NNs are particularly well appropriate to find underlying relationship in the environment characterized by complex, noisy, irrelevant or partial information. We also find that the nonlinear noise reduction methods work more effectively when the foreign exchange rates are more volatile.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Theodossiou, P.: The stochastic properties of major Canadian exchange rates. The Financial Review 29, 193–221 (1994)
Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting 14, 35–62 (1998)
Huang, W., Lai, K.K., Nakamori, Y., Wang, S.Y.: Forecasting foreign exchange rates with artificial neural networks: a review. International Journal of Information Technology & Decision Making 3, 145–165 (2004)
Soofi, A., Cao, L.: Nonlinear forecasting of noisy financial data. In: Soofi, A., Cao, L. (eds.) Modeling and Forecasting Financial Data: Techniques of Nonlinear Dynamics, pp. 455–465. Kluwer Academic Publishers, Boston (2002)
Davies, M.E.: Noise reduction schemes for chaotic time series. Physica D 79, 174–192 (1994)
Kostelich, E.J., Schreiber, T.: Noise reduction in chaotic time series data: A survey of common methods. Physical Review E 48, 1752–1800 (1993)
Grassberger, P., Hegger, R., Kantz, H., Schaffrath, C., Schreiber, T.: On noise reduction methods for chaotic data. CHAOS 3, 127 (1993)
Zhang, G., Hu, M.Y.: Neural network forecasting of the British Pound/US Dollar exchange rate. Journal of Management Science 26, 495–506 (1998)
Huang, W., Nakamori, Y., Wang, S.Y., Zhang, H.: Select the size of training set for financial forecasting with neural networks. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 879–884. Springer, Heidelberg (2005)
Harvey, D., Leybourne, S., Newbold, P.: Testing the Equality of Prediction Mean Squared Errors. International Journal of Forecasting 13, 281–291 (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Huang, W., Lai, K.K., Wang, S. (2007). Application of Neural Networks for Foreign Exchange Rates Forecasting with Noise Reduction. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2007. ICCS 2007. Lecture Notes in Computer Science, vol 4488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72586-2_65
Download citation
DOI: https://doi.org/10.1007/978-3-540-72586-2_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72585-5
Online ISBN: 978-3-540-72586-2
eBook Packages: Computer ScienceComputer Science (R0)