Seasonal modulation mixed models for time series forecasting

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dc.contributor.author Lee, Dae-Jin
dc.contributor.author Durbán, María
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2012-11-28T14:35:39Z
dc.date.available 2012-11-28T14:35:39Z
dc.date.issued 2012-11
dc.identifier.uri http://hdl.handle.net/10016/15980
dc.description.abstract We propose an extension of a seasonal modulation smooth model with P-splines for times series data using a mixed model formulation. A smooth trend with seasonality decomposition can be estimated simultaneously. We extend the model to consider the forecasting of new future observations in the mixed model framework. Two different approaches are used for forecasting in the context of mixed models, and the equivalence of both methods is shown. The methodology is illustrated with monthly sulphur dioxide (SO2) levels in a selection of monitoring sites in Europe from January 1990 to December 2001.
dc.description.sponsorship This research was funded by the Spanish Ministry of Science and Innovation (projects MTM2008-02901, and MTM2011-28285-C02-02). The research of Dae-Jin Lee was also funded by an NIH grant for the Superfund Metal Mixtures, Biomarkers and Neurodevelopment project 1PA2ES016454-01A2.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 12-19
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other P-splines
dc.subject.other Mixed models
dc.subject.other Times series forecasting
dc.subject.other Varying-coefficient models
dc.subject.other Harmonic regression
dc.title Seasonal modulation mixed models for time series forecasting
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. MTM2011-28285-C02-02
dc.type.version submitedVersion
dc.identifier.uxxi DT/0000000974
dc.identifier.repec ws122519
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