Evaluation of a Neuro-Fuzzy Scheme Forecasting Exchange Rates
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.
KeywordsForecast Exchange rate Fuzzy logic Fuzzy inference system Adaptive networks
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