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Pattern Detection and Discovery pp 214–226Cite as

Patterns of Dependencies in Dynamic Multivariate Data

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2447))

Abstract

In intensive care, clinical information systems permanently record more than one hundred time dependent variables. Besides the aim of recognising patterns like outliers, level changes and trends in such high-dimensional time series, it is important to reduce their dimension and to understand the possibly time-varying dependencies between the variables. We discuss statistical procedures which are able to detect patterns of dependencies within multivariate time series.

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Author information

Authors and Affiliations

  1. Department of Statistics, University of Dortmund, Vogelpothsweg 87, 44221, Dortmund, Germany

    Ursula Gather, Roland Fried & Claudia Becker

  2. Surgical Department, Community Hospital Dortmund, Beurhausstr. 40, 44137, Dortmund, Germany

    Michael Imhoff

Authors
  1. Ursula Gather
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  2. Roland Fried
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  3. Michael Imhoff
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  4. Claudia Becker
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Editor information

Editors and Affiliations

  1. Department of Mathematics, Imperial College of Science, Technology and Medicine, Huxley Building, 180 Queen’s Gate, SW7 2BZ, London, UK

    David J. Hand , Niall M. Adams  & Richard J. Bolton ,  & 

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© 2002 Springer-Verlag Berlin Heidelberg

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Gather, U., Fried, R., Imhoff, M., Becker, C. (2002). Patterns of Dependencies in Dynamic Multivariate Data. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds) Pattern Detection and Discovery. Lecture Notes in Computer Science(), vol 2447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45728-3_17

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  • DOI: https://doi.org/10.1007/3-540-45728-3_17

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44148-9

  • Online ISBN: 978-3-540-45728-2

  • eBook Packages: Springer Book Archive

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