Title :
Foreign exchange market forecasting using evolutionary fuzzy networks
Author :
Muhammad, A. ; King, G.A.
Author_Institution :
Southampton Inst., UK
Abstract :
The paper presents an evolutionary fuzzy network method for prediction in foreign exchange markets. The research chooses the financial data used in the Santa Fe time series forecasting competition, 1990-91 (Weigend, 1994). The choice of this data provides a comparative study of the previous attempts to forecast. Fuzzy systems not only provide the mechanism to integrate human linguistic knowledge into logical framework but also provides the means to extract fuzzy rules from an observed data set. For global optimisation, genetic algorithms are used to adapt the parameters of the fuzzy network in order to obtain the best performance
Keywords :
computational linguistics; financial data processing; forecasting theory; foreign exchange trading; fuzzy neural nets; fuzzy systems; genetic algorithms; prediction theory; time series; Santa Fe time series forecasting competition; data set; evolutionary fuzzy networks; financial data; foreign exchange market forecasting; fuzzy rule extraction; fuzzy systems; genetic algorithms; global optimisation; human linguistic knowledge; logical framework; parameter adaptation; prediction; Data mining; Economic forecasting; Fuzzy logic; Fuzzy systems; Genetic algorithms; Humans; Mathematical model; Neural networks; Nonlinear equations; Predictive models;
Conference_Titel :
Computational Intelligence for Financial Engineering (CIFEr), 1997., Proceedings of the IEEE/IAFE 1997
Conference_Location :
New York City, NY
Print_ISBN :
0-7803-4133-3
DOI :
10.1109/CIFER.1997.618939
