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dc.contributor.authorDe Klerk, J.
dc.date.accessioned2016-08-31T09:11:37Z
dc.date.available2016-08-31T09:11:37Z
dc.date.issued2015
dc.identifier.citationDe Klerk, J. 2015. Time series outlier detection using the trajectory matrix in singular spectrum analysis with outlier maps and ROBPCA. South African statistical journal, 49(1):61-76. [http://www.sastat.org.za/journal/information]en_US
dc.identifier.issn0038-271X
dc.identifier.urihttp://hdl.handle.net/10394/18478
dc.identifier.urihttp://reference.sabinet.co.za/webx/access/electronic_journals/sasj/sasj_v49_n1_a4.pdf
dc.description.abstractSingular spectrum analysis is a powerful non-parametric time series method that applies singular value decomposition to a Hankel structured matrix. The method can handle complex time series structures that include combinations of polynomials, sinusoids and exponentials. Outlier maps combined with robust principal component analysis is considered and shown to compare very favourably with existing time series methods to identify an additive time series outlier. The wellknown airline time series as well as a South African tourism time series are used to illustrate the usefulness of the methodologyen_US
dc.language.isoenen_US
dc.publisherSASA (South African Statistical Association)en_US
dc.subjectHankel matrixen_US
dc.subjectconvex hull peelingen_US
dc.subjectoutlier mapsen_US
dc.subjectrobust principal component analysisen_US
dc.subjectsingular spectrum analysisen_US
dc.titleTime series outlier detection using the trajectory matrix in singular spectrum analysis with outlier maps and ROBPCAen_US
dc.typeArticleen_US
dc.contributor.researchID23239603 - De Klerk, Jacques


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