Exchange rates forecasting using a hybrid fuzzy and neural network model

An-Pin Chen*, Hsio Yi Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Artificial neural networks (ANNs) are promising approaches for financial time series prediction and have been widely applied to handle finance problems because of its nonlinear structures. However, ANNs have some limitations in evaluating the output nodes as a result of single-point values. This study proposed a hybrid model, called Fuzzy BPN, consisting of backpropagation neural network (BPN) and fuzzy membership function for taking advantage of nonlinear features and interval values instead of the shortcoming of single-point estimation. In addition, the experimental processing can demonstrate the feasibility of applying the hybrid model-Fuzzy BPN and the empirical results show that Fuzzy BPN provides a useful alternative to exchange rate forecasting.

Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007
Pages758-763
Number of pages6
DOIs
StatePublished - 25 Sep 2007
Event1st IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007 - Honolulu, HI, United States
Duration: 1 Apr 20075 Apr 2007

Publication series

NameProceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007

Conference

Conference1st IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007
CountryUnited States
CityHonolulu, HI
Period1/04/075/04/07

Keywords

  • Backpropagation neural network
  • Exchange rate
  • Fuzzy membership function

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    Chen, A-P., & Lin, H. Y. (2007). Exchange rates forecasting using a hybrid fuzzy and neural network model. In Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007 (pp. 758-763). [4221376] (Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007). https://doi.org/10.1109/CIDM.2007.368952