Seasonal outliers in time series

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dc.contributor.author Kaiser, Regina
dc.contributor.author Maravall, Agustín
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2010-01-11T12:55:15Z
dc.date.available 2010-01-11T12:55:15Z
dc.date.issued 1999-06
dc.identifier.uri http://hdl.handle.net/10016/6333
dc.description.abstract In the analysis of time series, it is frequent to classify perturbations as Additive Outliers (AO) , Innovative Outliers (10), Level Shift (LS) outliers or Transitory Change (TC) outliers. When a time series with a clear seasonal behaviour is considered, this classification may be too restrictive since none of the four outlier types is adequate to model changes in the seasonal pattern of the series. In this paper, a new outlier type, the Seasonal level Shift (SLS), is introduced in order to complete the usual classification. The iterative procedure for the detection of outliers in Chen and Liu (1993) is extended to detect SLS outliers. We use simulations and real examples to assess the properties of the new type of outlier.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries 99-49-15
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other ARIMA models
dc.subject.other seasonality
dc.subject.other level shift
dc.subject.other outlier detection
dc.title Seasonal outliers in time series
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
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