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Finding Outliers in Linear and Nonlinear Time Series

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Robustness and Complex Data Structures

Abstract

The purpose of this contribution is to review outliers in both univariate and multivariate time series. The usual outlier types are presented in several frameworks including linear and nonlinear time series models. The key issues regarding identification of outliers and estimation of their effects in different settings are summarized.

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Acknowledgements

We gratefully acknowledge financial support by MCI grants SEJ2007/64500 and MTM2008-03010.

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Authors and Affiliations

  1. Departamento de Estadística, Universidad Carlos III de Madrid, C/Madrid, 126, 28903, Getafe, Madrid, Spain

    Pedro Galeano & Daniel Peña

Authors
  1. Pedro Galeano
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  2. Daniel Peña
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Corresponding author

Correspondence to Pedro Galeano .

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Editors and Affiliations

  1. Halle-Wittenberg, Faculty of Law and Economics, Martin-Luther-University, Große Steinstraße 73, Halle, 06099, Germany

    Claudia Becker

  2. Faculty of Statistics, TU Dortmund University, Vogelpothsweg 87, Dortmund, 44227, Germany

    Roland Fried

  3. Faculty of Statistics, TU Dortmund University, Vogelpothsweg 87, Dortmund, 44227, Germany

    Sonja Kuhnt

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Galeano, P., Peña, D. (2013). Finding Outliers in Linear and Nonlinear Time Series. In: Becker, C., Fried, R., Kuhnt, S. (eds) Robustness and Complex Data Structures. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35494-6_15

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