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Exchange Rate Forecasting Using Flexible Neural Trees

  • Yuehui Chen
  • Lizhi Peng
  • Ajith Abraham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. This paper proposes a Flexible Neural Tree (FNT) model for forecasting three major international currency exchange rates. Based on the pre-defined instruction sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using the Extended Compact Genetic Programming and the free parameters embedded in the neural tree are optimized by particle swarm optimization algorithm. Empirical results indicate that the proposed method is better than the conventional neural network forecasting models.

Keywords

Exchange Rate Particle Swarm Optimization Normalize Mean Square Error Foreign Exchange Rate Input Variable Selection 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yuehui Chen
    • 1
  • Lizhi Peng
    • 1
  • Ajith Abraham
    • 1
    • 2
  1. 1.School of Information Science and EngineeringJinan UniversityJinanP.R. China
  2. 2.IITA Professorship Program, School of Computer Science and Engg.Chung-Ang UniversitySeoulRepublic of Korea

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