Statistical fuzzy interval neural networks for currency exchange rate time series prediction

Authors:
Yan-Qing Zhang;Xuhui Wan
Affiliations:
Department of Computer Science, Georgia State University, P.O. Box 3994, Atlanta, GA 30302-3994, USA;Department of Computer Science, Georgia State University, P.O. Box 3994, Atlanta, GA 30302-3994, USA
Venue:
Applied Soft Computing
Year:
2007

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Abstract

In this paper, the statistical fuzzy interval neural network with statistical interval input and output values is proposed to perform statistical fuzzy knowledge discovery and the currency exchange rate prediction. Time series data sets are grouped into time series data granules with statistical intervals. The statistical interval data sets including week-based averages, maximum errors of estimate and standard deviations are used to train the fuzzy interval neural network to discover fuzzy IF-THEN rules. The output of the fuzzy interval neural network is an interval value with certain percent confidence. Simulations are completed in terms of the exchange rates between US Dollar and other three currencies (Japanese Yen, British Pound and Hong Kong Dollar). The simulation results show that the fuzzy interval neural network can provide more tolerant prediction results.