Exchange-Rates Forecasting: A Hybrid Algorithm Based on Genetically Optimized Adaptive Neural Networks |
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Authors: | Andreas S. Andreou Efstratios F. Georgopoulos Spiridon D. Likothanassis |
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Affiliation: | (1) Department of Computer Science, University of Cyprus, 75 Kallipoleos Str., P.O. Box 20537, CY1678 Nicosia, Cyprus;(2) Department of Computer Engineering & Informatics, University of Patras, Patras, 26500, Greece;(3) Computer Technology Institute, 3 Kolokotroni Str., 2622 1 Patras, Greece |
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Abstract: | The use of neural networks trained by a new hybrid algorithm is employed on forecasting the Greek Foreign Exchange-Rate Market. Four major currencies, namely the U.S. Dollar (USD), the Deutsche Mark (DEM), the French Franc (FF) and the British Pound (GBP), versus the Greek Drachma, were used as experimental data. The proposed algorithm combines genetic algorithms and a training method based on the localized Extended Kalman Filter (EKF), in order to evolve the structure and train Multi-Layered Perceptron (MLP) neural networks. The goal of this effort is to predict, as accurately as possible, exchange-rates future behavior. Simulation results show that the method gives highly successful results, while the diversification of the structure between the four currencies has no effect on the performance. |
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Keywords: | exchange-rates neural networks genetic algorithms filtering forecasting |
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