Neural Computing and Applications
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
Adaptive smoothing neural networks in foreign exchange rate forecasting
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Dynamic population variation in genetic programming
Information Sciences: an International Journal
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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 Hierarchical Radial Basis Function Network (HiRBF) model for forecasting three major international currency exchange rates. Based on the pre-defined instruction sets, HRBF model can be created and evolved. The HRBF structure is developed using the Extended Compact Genetic Programming (ECGP) and the free parameters embedded in the tree are optimized by the Degraded Ceiling Algorithm (DCA). Empirical results indicate that the proposed method is better than the conventional neural network and RBF networks forecasting models.