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dc.contributor.authorMouton, J.
dc.contributor.authorHoffman, A.J.
dc.date.accessioned2017-02-02T09:25:37Z
dc.date.available2017-02-02T09:25:37Z
dc.date.issued2014
dc.identifier.citationMouton, J. & Hoffman, A.J. 2014. Combining empirical mode decomposition with neural networks for the prediction of exchange rates. Proceedings of the International Conference on Neural Computation Theory and Applications (NCTA-2014):244-249. [http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220%2f0005130702440249]en_US
dc.identifier.isbn978-989-758-054-3
dc.identifier.urihttp://hdl.handle.net/10394/19945
dc.identifier.urihttp://dx.doi.org/10.5220/0005130702440249
dc.identifier.urihttp://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0005130702440249
dc.description.abstractThis paper proposes a neural network based model applied to empirical mode decomposition (EMD) filtered data for multi-step-ahead prediction of exchange rates. EMD is used to decompose the returns of exchange rates into intrinsic mode functions (IMFs) which are partially recomposed to produce a low-pass filtered time series. This series is used to train a neural network for multi-step-ahead prediction. Out-of-sample tests on EUR/USD and USD/JPY rates show superior performance compared to random walk and neural network models that do not employ EMD filtering. The novel approach of using EMD as a filtering technique in combination with neural networks consistently delivers higher returns on investment and demonstrates its utility in multi-step-ahead predictionen_US
dc.language.isoenen_US
dc.publisherSCITEPRESS (Science and Technology Publications, Lda.)en_US
dc.subjectEmpirical Mode Decomposition (EMD)en_US
dc.subjectArtificial neural networken_US
dc.subjectForeign exchange rate forecastingen_US
dc.titleCombining empirical mode decomposition with neural networks for the prediction of exchange ratesen_US
dc.typePresentationen_US
dc.contributor.researchID10196978 - Hoffman, Alwyn Jakobus


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