Statistical fuzzy interval neural networks for currency exchange rate time series prediction |
| |
Authors: | Yan-Qing Zhang Xuhui Wan |
| |
Affiliation: | aDepartment of Computer Science, Georgia State University, P.O. Box 3994, Atlanta, GA 30302-3994, USA |
| |
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. |
| |
Keywords: | Fuzzy data mining Fuzzy neural networks Granular computing Interval computing Time series prediction Currency exchange rates Statistical computing |
本文献已被 ScienceDirect 等数据库收录! |
|