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Using robust FPCA to identify outliers in functional time series, with applications to the electricity market
dc.contributor.author | Vilar, Juan M. | |
dc.contributor.author | Raña, Paula | |
dc.contributor.author | Aneiros Pérez, Germán | |
dc.date.accessioned | 2024-06-28T08:54:06Z | |
dc.date.available | 2024-06-28T08:54:06Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Vilar, Juan M.; Raña, Paula; Aneiros, Germán. “Using robust FPCA to identify outliers in functional time series, with applications to the electricity market”. SORT-Statistics and Operations Research Transactions, 2016, Vol. 40, Num. 2, pp. 321-348, https://raco.cat/index.php/SORT/article/view/316148 | es_ES |
dc.identifier.issn | 1696-2281 | |
dc.identifier.uri | http://hdl.handle.net/2183/37540 | |
dc.description | From February 2013 articles are under a Creative Commons license: CC BY-NC-ND | es_ES |
dc.description.abstract | [Abstract]: This 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. © 2016, Institut d'Estadistica de Catalunya. | es_ES |
dc.description.sponsorship | The authors wish to thank two anonymous referees for their helpful comments andsuggestions, which greatly improved the quality of this paper. This research was par-tially supported by Grants MTM2014-52876-R from Spanish Ministerio de Econom ́ıay Competitividad, and CN2012/130 from Xunta de Galicia. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Institut d'Estadistica de Catalunya | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2014-52876-R/ES/INFERENCIA ESTADISTICA COMPLEJA Y DE ALTA DIMENSION: EN GENOMICA, NEUROCIENCIA, ONCOLOGIA, MATERIALES COMPLEJOS, MALHERBOLOGIA, MEDIO AMBIENTE, ENERGIA Y APLICACIONES INDUSTRI | es_ES |
dc.relation.uri | https://raco.cat/index.php/SORT/article/view/316148 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Electricity demand and price | es_ES |
dc.subject | Functional data analysis | es_ES |
dc.subject | Functional principal component analysis | es_ES |
dc.subject | Functional time series | es_ES |
dc.subject | Outlier detection | es_ES |
dc.title | Using robust FPCA to identify outliers in functional time series, with applications to the electricity market | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | SORT-Statistics and Operations Research Transactions | es_ES |
UDC.volume | 40 | es_ES |
UDC.issue | 2 | es_ES |
UDC.startPage | 321 | es_ES |
UDC.endPage | 348 | es_ES |
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