Time series outlier detection using the trajectory matrix in singular spectrum analysis with outlier maps and ROBPCA
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Date
Authors
De Klerk, J.
Journal Title
Journal ISSN
Volume Title
Publisher
SASA (South African Statistical Association)
Abstract
Singular 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 methodology
Description
Citation
De 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]