Abstract
Recently, various variants of the self-organsing map (SOM) have been proposed for modelling and predicting time series. However, most of them are based on lattice structure. In this paper, a hybrid neural model combining neural gas (NG) and mixture autoregressive models is developed for forecasting foreign exchange (FX) rates. It takes advantage of some NG features (i.e. neighbourhood rankings) and incorporates mixture autoregressive models, for effectively modelling and forecasting non-stationary and nonlinear time series. Experiments on FX rates are presented and the results show that the proposed model performs significantly better than other methods, in terms of normalised root-mean-squared-error and correct trend prediction percentage.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tsay, R.S.: Conditional heteroscedastic time series models. Journal of the American Statistical Association 82(398), 590–604 (1987)
Huang, W., Lai, K.K., Nakamori, Y., Wang, S.: Forecasting foreign exchange rates with artificial neural networks: A review. International Journal of Information Technology & Decision Making 3(1), 145–165 (2004)
Tsay, R.S.: Analysis of Financial Time Series. Wiley-Interscience, Hoboken (2005)
Lapedes, A., Farber, R.: Nonlinear signal processing using neural networks: Prediction and system modelling. Technical Report, LA-UR-87-2662, Los Alamos National Laboratory, Los Alamos, NM (1987)
Zhang, G., Eddy Patuwo, B., Hu, Y.M.: Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting 14(1), 35–62 (1998)
Crone, S.F., Hibon, M., Nikolopoulos, K.: Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction. International Journal of Forecasting 27(3), 635–660 (2011)
Yin, H., Ni, H.: Generalized self-organizing mixture autoregressive model for modeling financial time series. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009, Part I. LNCS, vol. 5768, pp. 577–586. Springer, Heidelberg (2009)
Chappell, G.J., Taylor, J.G.: The temporal Kohonen map. Neural Networks 6(3), 441–445 (1993)
Varsta, M., Milln, J.D.R., Heikkonen, J.: A recurrent self-organizing map for temporal sequence processing. In: Proceedings of the 7th International Conference on Artificial Neural Networks, Lausanne, Switzerland, pp. 421–426 (1997)
Voegtlin, T.: Recursive self-organizing maps. Neural Networks 15(8-9), 979–991 (2002)
Martinetz, T.M., Berkovich, S.G., Schulten, K.J.: “Neural-gas” network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks 4(4), 558–569 (1993)
Lampinen, J., Oja, E., SCIA: Self-organizing maps for spatial and temporal AR models. In: Proceedings of the 6th Scandinavian Conference on Image Analysis (SCIA), Helsinki, Finland, pp. 120–127 (1989)
Ni, H., Yin, H.: A self-organising mixture autoregressive network for FX time series modelling and prediction. Neurocomputing 72(16-18), 3529–3537 (2009)
De Gooijer, J.G., Hyndman, R.J.: 25 years of time series forecasting. International Journal of Forecasting 22(3), 443–473 (2006)
Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. International Journal of Forecasting 22(4), 679–688 (2006)
Barreto, G.A., Araujo, A.F.R.: Identification and control of yynamical systems using the self-organizing map. IEEE Transactions on Neural Networks 15(5), 1244–1259 (2004)
Li, D., Han, M., Wang, J.: Chaotic time series prediction based on a novel robust echo state network. IEEE Transactions on Neural Networks and Learning Systems 23(5), 787–799 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ouyang, Y., Yin, H. (2013). Forecasting Financial Time Series Using a Hybrid Self-Organising Neural Model. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-41278-3_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41277-6
Online ISBN: 978-3-642-41278-3
eBook Packages: Computer ScienceComputer Science (R0)