Forecasting currency exchange rates are an important financial problem that is receiving increasing attention, especially because of its intrinsic difficulty and practical applications. During the last few years, a number of nonlinear models have been proposed for obtaining accurate prediction results, in an attempt to ameliorate the performance of the traditional linear approaches. Among them, neural network models have been used with encouraging results. This paper presents improved neural network and fuzzy models used for exchange rate prediction. Several approaches, including multi-layer perceptions, radial basis functions, dynamic neural networks and neuro-fuzzy systems, have been proposed and discussed. Their performances for one-step and multiple step ahead predictions have been evaluated through a study, using real exchange daily rate values of the US Dollar vs. British Pound.
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ID="A1" Correspondence and offprint requests to: Dr V. Kodogiannis, Mechatronics Group, Department of Computer Science, University of Westminster, London HAI 3TP, UK. Email: kodogiv@wmin.ac.uk
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Kodogiannis, V., Lolis, A. Forecasting Financial Time Series using Neural Network and Fuzzy System-based Techniques. Neural Comput Applic 11, 90–102 (2002). https://doi.org/10.1007/s005210200021
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DOI: https://doi.org/10.1007/s005210200021