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dc.contributor.authorVilar, Juan M.
dc.contributor.authorRaña, Paula
dc.contributor.authorAneiros, Germán
dc.identifier.citationVilar, J. M.; Raña, P.; Aneiros, G. Using robust FPCA to identify outliers in functional time series, with applications to the electricity market. "SORT", 19 Desembre 2016, vol. 1, p. 321-348.
dc.description.abstractThis study proposes two methods for detecting outliers in functional time series. Both methods take dependence in the data into account and are based on robust functional principal component analysis. One method seeks outliers in the series of projections on the first principal component. The other obtains uncontaminated forecasts for each data set and determines that those observations whose residuals have an unusually high norm are considered outliers. A simulation study shows the performance of these proposed procedures and the need to take dependence in the time series into account. Finally, the usefulness of our methodology is illustrated in two real datasets from the electricity market: daily curves of electricity demand and price in mainland Spain, for the year 2012.
dc.format.extent28 p.
dc.publisherInstitut d'Estadística de Catalunya
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
dc.subject.otherFunctional data analysis
dc.subject.otherfunctional principal component analysis
dc.subject.otherfunctional time series
dc.subject.otheroutlier detection
dc.subject.otherelectricity demand and price
dc.titleUsing robust FPCA to identify outliers in functional time series, with applications to the electricity market
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::62 Statistics::62H Multivariate analysis
dc.subject.amsClassificació AMS::62 Statistics::62M Inference from stochastic processes
dc.rights.accessOpen Access

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