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Exploring ICA for time series decomposition

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorGarcía-Ferrer, Antonio
dc.contributor.authorGonzález-Prieto, Ester
dc.contributor.authorPeña, Daniel
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned2011-05-27T10:13:08Z
dc.date.available2011-05-27T10:13:08Z
dc.date.issued2011-05
dc.description.abstractIn this paper, we apply independent component analysis (ICA) for prediction and signal extraction in multivariate time series data. We compare the performance of three different ICA procedures, JADE, SOBI, and FOTBI that estimate the components exploiting either the non-Gaussianity, or the temporal structure of the data, or combining both, non-Gaussianity as well as temporal dependence. Some Monte Carlo simulation experiments are carried out to investigate the performance of these algorithms in order to extract components such as trend, cycle, and seasonal components. Moreover, we empirically test the performance of those three ICA procedures on capturing the dynamic relationships among the industrial production index (IPI) time series of four European countries. We also compare the accuracy of the IPI time series forecasts using a few JADE, SOBI, and FOTBI components, at different time horizons. According to the results, FOTBI seems to be a good starting point for automatic time series signal extraction procedures, and it also provides quite accurate forecasts for the IPIs.
dc.format.mimetypeapplication/pdf
dc.identifier.repecws111611
dc.identifier.urihttps://hdl.handle.net/10016/11285
dc.identifier.uxxiDT/0000000939
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries11-11
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEstadística
dc.subject.otherICA
dc.subject.otherSignal extraction
dc.subject.otherMultivariate time series
dc.subject.otherForecasting
dc.titleExploring ICA for time series decomposition
dc.typeworking paper*
dspace.entity.typePublication
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