An Elman Model Based on GMDH Algorithm for Exchange Rate Forecasting

Qiumin LI, Yixiang TIAN, Gaoxun ZHANG

Abstract


Since the Elman Neural Networks was proposed, it has attracted wide attention. This method has fast convergence and high prediction accuracy. In this study, a new hybrid model that combines the Elman Neural Networks and the group method of data handling (GMDH) is used to forecast the exchange rate. The GMDH algorithm is used for system modeling. Input variables are selected by the external standards. Based on the output of the GMDH algorithm, valid input variables can be used as an input for the Elman Neural Networks for time series prediction. The empirical results show that the new hybrid algorithm is a useful tool. 


Keywords


Elman Neural Networks; GMDH; Forecast

Full Text:

PDF

References


Abdel-Aal, R., Elhadidy, M., & Shaahid, S. (2009). Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks. Renewable Energy, 34(7), 1686–1699.

Alvarez-Diaz, M., & Alvarez, A. (2005). Genetic multimodel composite forecast for non-linear forecasting of ex-change rates. Empirical Economics, 30(3), 643-663.

Alvarez-Diaz, M., & Alvarez, A. (2007). Forecasting exchange rates using an evolutionary neural network. Applied Financial Economics Letters, 3(1), 5-9.

Barron, A. R. (1988). Statistical learning networks: A unifying view (pp. 192-203). in Proceedings of the 20th symposium Computing Science and Statistics. Alexandria: American Statistical Association.

Cassel, G. (1930). Money and foreign exchange after 1914. London：Macmillan.

De Grauwe, P., & Dewachter, H. (1993)．A chaotic model of the exchange rate：The role of fundamentalists and chartists．Open Economies Review, 4(4), 351-379.

Dorn, M., Braga, A. L. S., Llanos, C. H., & Coelho, L. S. (2012). A GMDH polynomial neural network-based method to predict approximate three-dimensional structures of polypeptides. Expert Systems with Applications, 39, 12268–12279.

Elder, J. F. (1996). A Statistical Perspective on Knowledge Discovery in Databases.California: AAAI Press, MIT Press.

Elman, J. L. (1990). Finding structure in time. Cognitive Science, (02), 179-211.

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417.

Farlow, S. J. (1984). Self-organizing methods in modeling. Marcel Dekker, New York and Basel.

Francis, E. H. T., & Cao, L. J. (2001). Application of support vector machines in financial time series forecasting. International Journal of Management Science, 29, 309-317.

Hsieh, D. A. (1989). Testing for nonlinear dependence in daily foreign exchange rates. Journal of Busines, 62(3), 329-368.

Ivakhnenko, A. G. (1970). Heuristic self-organization on problems of engineering cybernetics. Automatic, 6(3), 207-219.

Ivakhnenko, A. G., & Ivakhnenko, G. A. (1995). The review of problems solvable with algorithms of the Group Method of Data Handling (GMDH). Pattern Recognition and Image Analysis, 5(4), 527–535.

Keynes, J. M. (1923). A track on monetary reform. London: Macmillan.

Kondo, T., Pandya, A. S., & Zurada, J. M. (1999). Logistic GMDH-type neural networks and their application to the identification of the X-ray film characteristic curve. In Proceedings of the IEEE international conference on systems, man and cybernetics, 1, 437–442.

Mandelbrot, B. B. (1963). The variation of certain speculative prices．Journal of Business, 36(4), 394-419.

Mehrara, M., Moeini, A., Ahrari, M., & Erfanifard, A. (2009). Investigating the efficiency in oil futures market based on GMDH approach. Expert Systems with Applications, 36, 7479-7483.

Muller, J. A., & Lemke, F. (2000). Self-organizing data mining. Dresden, Berlin. Libri Books.

Nikkinen, J., Pynnonen, S., Ranta, M., & Vahamaa, S. (2011). Cross-dynamics of exchange rate expectations: a wavelet analysis. International Journal of Finance & Economics, 16(3), 205–217.

Onwubolu, G. C. (2008). Design of hybrid differential evolution and group method of data handling networks for modeling and prediction. Information Sci, 178, 3616– 3634.

Samsudin, R., Saad, P., & Shabri, A. (2011). A hybrid GMDH and least squares support vector machines in time series forecasting. Neural Network World, 21(3), 251-268.

Saunders, C., Gammerman, A., & Vovk, V. (1998). Ridge regression learning algorithm in dual variables (pp.515-521). Proceedings of the 15th International Conference on Machine Learning ICML-98, Madison, WI, USA.

Shuhaida, I., Shabri, A., & Samsudin, R. (2011). A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting. Expert Systems With Applications, 38, 10574- 10578.

Suykens, J. A. K. (2001). Nonlinear modeling and support vector machines, IEEE Instrumentation and Measurement Technology Conference, Budapest, Hungary, 287–294.

Vapnik, V. (1998). Statistical learning theory. New York, NY.: JohnWiley.




DOI: http://dx.doi.org/10.3968/5666

Refbacks

  • There are currently no refbacks.


Copyright (c)



Share us to:   

Reminder

  • We are currently accepting submissions via email only.

    The registration and online submission functions have been disabled.

    Please send your manuscripts to mse@cscanada.net，or mse@cscanada.org  for consideration.

    We look forward to receiving your work.

 


We only use three mailboxes as follows to deal with issues about paper acceptance, payment and submission of electronic versions of our journals to databases:
caooc@hotmail.com; mse@cscanada.net; mse@cscanada.org

 Articles published in Management Science and Engineering are licensed under Creative Commons Attribution 4.0 (CC-BY).

 MANAGEMENT SCIENCE AND ENGINEERING Editorial Office

Address:1055 Rue Lucien-L'Allier, Unit #772, Montreal, QC H3G 3C4, Canada.

Telephone: 1-514-558 6138
Http://www.cscanada.net Http://www.cscanada.org

Copyright © 2010 Canadian Research & Development Centre of Sciences and Cultures