Advertisement

Evaluation of a Neuro-Fuzzy Scheme Forecasting Exchange Rates

  • George Tselentis
  • George Dounias
Chapter
Part of the Applied Optimization book series (APOP, volume 19)

Abstract

A Neuro-fuzzy windowing scheme is applied to predict exchange rates. By using a hybrid learning procedure an input-output mapping can be constructed based on training data pairs. The scheme applies the ANFIS (Adaptive-Network-based Fuzzy Inference System) algorithm which is based on the least-squares method and the back-propagation gradient descent for identifying linear and nonlinear parameters, respectively, in a Sugeno-type fuzzy inference system. The retraining is performed in a moving window of input data, thus keeping track of the latest changes. An evaluation of the scheme for a short-term prediction interval is performed using real exchange rates of USD vs. GRD, DEM, FRF and ECU.

Keywords

Forecast Exchange rate Fuzzy logic Fuzzy inference system Adaptive networks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bosarge W. Jr (1993), “Adaptive Processes to Exploit the Non-linear structure of Financial Markets”, in: R. Trippi and E. Turban (eds.), Neural Networks in Finance and Investing, Probus Publishing, 465–492.Google Scholar
  2. Iebeling, K. and Milton B. (1996), “Designing a neural network for forecasting financial and economic time series”, Neurocomputing 10/3, 215–236.Google Scholar
  3. Jang R. (1991), “Fuzzy modeling using generalized neural networks”, Prot: 4 th IFSA World Congress. Google Scholar
  4. Kane R. and Milgram M. (1994), “Financial forecasting and rules extraction from trained networks”, Proceedings of the 1994 IEEE International Conference on Neural Networks, Orlando, FL, USA, 3190–3195.CrossRefGoogle Scholar
  5. Medsker L., Turban E. and Trippi, R. (1993), “Neural network fundamentals for financial analysts”, in: R. Trippi and E. Turban (eds.), Neural Networks in Finance and Investing, Probus Publishing, 3–27.Google Scholar
  6. Parker D.B. (1987), “Optimal algorithms for adaptive networks: Second order back propagation, second order direct propagation and second order Hebbian learning”, Proc. IEEE Int. Conf. Neural Networks, 593–600.Google Scholar
  7. Refenes A. (1993), “Constructive learning and its application to currency exchange rate forecasting”, in: R. Trippi and E. Turban (eds.), Neural Networks in Finance and Investing, Probus Publishing, 465–492.Google Scholar
  8. Jang, R. (1993), “ANFIS: Adaptive-Network-Based Fuzzy Inference System”, IEEE Transactions on Systems, Man and Cybernetics 23/3, 665–685.CrossRefGoogle Scholar
  9. Takagi H. and Sugeno, M. (1983), “Derivation of fuzzy control rules from human operator’s control actions”, Proc. IF AC Symp. Fuzzy Inform., Knowledge Representation and Decision Analysis, 55–60.Google Scholar
  10. Weigend, A.S., Rumelhar D.E. and Huberman, B.A. (1990), “Back propagation weight-elimination and time series prediction”, in: Touretzky D., Hinton G. and T. Sejnowski (eds.), Proc. 1990 Connectionist Models Summer School, Carnegie Mellon University, 105–116.Google Scholar
  11. Weigend, A.S., Rumelhar D.E. and Huberman, B.A. (1991), “Generalization by weight-elimination with application to forecasting” in: D. Touretzky (ed.), Advances in Neural Information Processing Systems III, San Mateo, 875–882.Google Scholar
  12. Yaser, A. and Amir, F. (1996), “Introduction to financial forecasting”, Applied Intelligence 6/3, 205–213.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • George Tselentis
    • 1
  • George Dounias
    • 1
  1. 1.Department of Production and Management EngineeringTechnical University of CreteChaniaGreece

Personalised recommendations