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Detection of outlier patches in autoregressive time series

Author

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  • Justel, A.
  • Tsay, Ruey S.

Abstract

This paper proposed a procedure to identify patches of outliers in an autoregressive process. The procedure is an improvement over the existing outlier detection methods via Gibbs sampling. It identifies the beginning and end of possible outlier patches using the existing Gibbs sampling, then carries out and adaptive procedure with block interpolation to handle patches of outliers. Empirical and simulated examples show that the proposed procedure is effective in handling masking and swamping effects caused by multiple outliers. The real example also shows that the standard Gibbs sampling to outlier detection may encounter severe masking and swamping effects in practice.

Suggested Citation

  • Justel, A. & Tsay, Ruey S., 1998. "Detection of outlier patches in autoregressive time series," DES - Working Papers. Statistics and Econometrics. WS 9821, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:9821
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    References listed on IDEAS

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    1. Robert E. McCulloch & Ruey S. Tsay, 1994. "Bayesian Analysis Of Autoregressive Time Series Via The Gibbs Sampler," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(2), pages 235-250, March.
    2. Sánchez, María Jesús, 1997. "The identification of multiple outliers in arima models," DES - Working Papers. Statistics and Econometrics. WS 6220, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Pena, Daniel, 1990. "Influential Observations in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 235-241, April.
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    Cited by:

    1. Tsay, Ruey S. & Pankratz, Alan E., 1998. "Outliers in multivariate time series," DES - Working Papers. Statistics and Econometrics. WS 6285, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Bauer, Marcus & Gather, Ursula & Imhoff, Michael, 1999. "The identification of multiple outliers in online monitoring data," Technical Reports 1999,29, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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    Keywords

    Multiple outliers;

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