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dc.contributor.authorAue, Alexander
dc.contributor.authorDubart Norinho, Diogo
dc.contributor.authorHörmann, Siegfried
dc.date.accessioned2014-04-25T12:00:01Z
dc.date.accessioned2016-10-05T14:13:58Z
dc.date.available2014-04-25T12:00:01Z
dc.date.available2016-10-05T14:13:58Z
dc.date.issued2014-04-25
dc.identifier.urihttp://publications.mfo.de/handle/mfo/1077
dc.descriptionResearch in Pairs 2013en_US
dc.description.abstractThis paper addresses the prediction of stationary functional time series. Existing contributions to this problem have largely focused on the special case of first-order functional autoregressive processes because of their technical tractability and the current lack of advanced functional time series methodology. It is shown here how standard multivariate prediction techniques can be utilized in this context. The connection between functional and multivariate predictions is made precise for the important case of vector and functional autoregressions. The proposed method is easy to implement, making use of existing statistical software packages, and may therefore be attractive to a broader, possibly non-academic, audience. Its practical applicability is enhanced through the introduction of a novel functional final prediction error model selection criterion that allows for an automatic determination of the lag structure and the dimensionality of the model. The usefulness of the proposed methodology is demonstrated in a simulation study and an application to environmental data, namely the prediction of daily pollution curves describing the concentration of particulate matter in ambient air. It is found that the proposed prediction method often significantly outperforms existing methods.en_US
dc.language.isoenen_US
dc.publisherMathematisches Forschungsinstitut Oberwolfachen_US
dc.relation.ispartofseriesOberwolfach Preprints;2014,06
dc.subjectDimension reductionen_US
dc.subjectFinal predictionen_US
dc.subjecterror Forecastingen_US
dc.subjectFunctional autoregressionsen_US
dc.subjectFunctional principal componentsen_US
dc.subjectFunctional time seriesen_US
dc.subjectParticulate matteren_US
dc.subjectVector autoregressionsen_US
dc.titleOn the prediction of stationary functional time seriesen_US
dc.typePreprinten_US
dc.identifier.doi10.14760/OWP-2014-06
local.scientificprogramResearch in Pairs 2013
local.series.idOWP-2014-06
local.subject.msc62
local.subject.msc60


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