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The identification of multiple outliers in online monitoring data

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  • Bauer, Marcus
  • Gather, Ursula
  • Imhoff, Michael

Abstract

We present a robust graphical procedure for routine detection of isolated and patchy outliers in univariate time series. This procedure is suitable for retrospective as well as for online identification of outliers. It is based on a phase space reconstruction of the time series which allows to regard the time series as a multivariate sample with identically distributed but non independent observations. Thus, multivariate outlier identifiers can be transferred into the context of time series which is done here. Some applications to online monitoring data from intensive care are given.

Suggested Citation

  • 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.
    • Handle: RePEc:zbw:sfb475:199929
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    File URL: https://www.econstor.eu/bitstream/10419/77375/2/1999-29.pdf
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    References listed on IDEAS

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    1. Johannes Ledolter, 1990. "Outlier Diagnostics In Time Series Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(4), pages 317-324, July.
    2. John F. MacGregor, 1997. "Using On‐Line Process Data to Improve Quality: Challenges for Statisticians," International Statistical Review, International Statistical Institute, vol. 65(3), pages 309-323, December.
    3. Pena, Daniel, 1990. "Influential Observations in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 235-241, April.
    4. Harvey, Andrew C & Koopman, Siem Jan, 1992. "Diagnostic Checking of Unobserved-Components Time Series Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 377-389, October.
    5. Justel, A. & Peña, Daniel & 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.
    6. Tong, Howell & Yao, Qiwei, 1994. "On prediction and chaos in stochastic systems," LSE Research Online Documents on Economics 6410, London School of Economics and Political Science, LSE Library.
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