Neural-network-based fuzzy group forecasting with application to foreign exchange rates prediction

Abstract

This study proposes a novel neural-network-based fuzzy group forecasting model for foreign exchange rates prediction. In the proposed model, some single neural network models are first used as predictors for foreign exchange rates prediction. Then these single prediction results produced by each single neural network models are fuzzified into some fuzzy prediction representations. Subsequently, these fuzzified prediction representations are aggregated into a fuzzy group consensus, i.e., aggregated fuzzy prediction representation. Finally, the aggregated prediction representation is defuzzified into a crisp value as the final prediction results. For illustration and testing purposes, a typical numerical example and three typical foreign exchange rates prediction experiments are presented. Experimental results reveal that the proposed model can significantly improve the prediction performance for foreign exchange rates. © Springer-Verlag Berlin Heidelberg 2007.