Evolutionary data selection for enhancing models of intraday forex time series

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dc.contributor.authorMayo, Michael
dc.coverage.spatialConference held at Malaga, Spainen_NZ
dc.date.accessioned2012-02-22T03:40:52Z
dc.date.available2012-02-22T03:40:52Z
dc.date.issued2012
dc.description.abstractThe hypothesis in this paper is that a significant amount of intraday market data is either noise or redundant, and that if it is eliminated, then predictive models built using the remaining intraday data will be more accurate. To test this hypothesis, we use an evolutionary method (called Evolutionary Data Selection, EDS) to selectively remove out portions of training data that is to be made available to an intraday market predictor. After performing experiments in which data-selected and non-data-selected versions of the same predictive models are compared, it is shown that EDS is effective and does indeed boost predictor accuracy. It is also shown in the paper that building multiple models using EDS and placing them into an ensemble further increases performance. The datasets for evaluation are large intraday forex time series, specifically series from the EUR/USD, the USD/JPY and the EUR/JPY markets, and predictive models for two primary tasks per market are built: intraday return prediction and intraday volatility prediction.en_NZ
dc.format.mimetypeapplication/pdf
dc.identifier.citationMayo, M. (2012). Evolutionary data selection for enhancing models of intraday forex time series. In C. Di Chio et al. (Eds.): EvoApplications 2012, LNCS 7248, pp.184--193. Springer, Heidelberg.en_NZ
dc.identifier.doi10.1007/978-3-642-29178-4_19en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/6048
dc.language.isoen
dc.publisherSpringeren_NZ
dc.relation.isPartOfProc Applications of Evolutionary Computation: EvoApplications 2012en_NZ
dc.relation.urihttp://evostar.dei.uc.pt/2012/call-for-contributions/evoapplications/en_NZ
dc.rights© 2012 The Authoren_NZ
dc.subjectintradayen_NZ
dc.subjectforexen_NZ
dc.subjectsteady stateen_NZ
dc.subjectgenetic algorithmen_NZ
dc.subjectinstance selectionen_NZ
dc.subjectdata miningen_NZ
dc.subjectreturn predictionen_NZ
dc.subjectvolatility predictionen_NZ
dc.subjectMachine learning
dc.titleEvolutionary data selection for enhancing models of intraday forex time seriesen_NZ
dc.typeConference Contributionen_NZ
pubs.begin-page184en_NZ
pubs.elements-id21858
pubs.end-page193en_NZ
pubs.finish-date2012-04-13en_NZ
pubs.start-date2012-04-11en_NZ
pubs.volumeLNCS 7248en_NZ
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