Forecasting Exchange Rates: A Neuro-Fuzzy Approach

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dc.contributor.authorAlizadeh, Meysam
dc.contributor.authorRada, Roy
dc.contributor.authorBalagh, Akram Khaleghei Ghoshe
dc.contributor.authorEsfahani, Mir Mehdi Seyyed
dc.date.accessioned2020-11-13T18:33:02Z
dc.date.available2020-11-13T18:33:02Z
dc.description.abstractThis paper presents an adaptive neuro-fuzzy inference system (ANFIS) for USD/JPY exchange rates forecasting. Previous work often used time series techniques and neural networks (NN). ANFIS can be used to better explain solutions to users than completely black-box models, such as NN. The proposed neuro-fuzzy rule based system applies some technical and fundamental indexes as input variables. In order to generate membership functions (MFs), we make use of fuzzy clustering of the output space. The neuro-fuzzy model is tested with 28 candidate input variables for both currencies. For the purpose of comparison, Sugeno-Yasukawa model, feedforward multi-layer neural network, and multiple regression are benchmarked. The comparison demonstrates that the presented algorithm shows its superiority in terms of prediction error minimization, robustness and flexibilityen_US
dc.format.extent6 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/m256wt-btfz
dc.identifier.citationMeysam Alizadeh, Roy Rada, Akram Khaleghei Ghoshe Balagh and Mir Mehdi Seyyed Esfahani, Forecasting Exchange Rates: A Neuro-Fuzzy Approach, https://www.researchgate.net/publication/221399563_Forecasting_Exchange_Rates_A_Neuro-Fuzzy_Approachen_US
dc.identifier.urihttp://hdl.handle.net/11603/20057
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.titleForecasting Exchange Rates: A Neuro-Fuzzy Approachen_US
dc.typeTexten_US

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