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International Conference on Artificial Immune Systems

ICARIS 2006: Artificial Immune Systems pp 376–389Cite as

Theoretical Basis of Novelty Detection in Time Series Using Negative Selection Algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4163))

Abstract

Theoretical basis of Novelty Detection in Time Series and its relationships with State Space Reconstruction are discussed. It is shown that the methods for estimation of optimal state-space reconstruction parameters may be used for the estimation of immunological novelty detection system’s parameters. This is illustrated with a V-detector system detecting novelties in Mackey-Glass time series.

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Author information

Authors and Affiliations

  1. Wrocław University of Technology, 27 Wybrzeże Wyspiańskiego, 50-370, Wrocław, Poland

    Rafał Pasek

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  1. Rafał Pasek
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Editors and Affiliations

  1. IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium

    Hugues Bersini

  2. Instituto Gulbenkian de Ciencia, Apartado 14, 2781-901, Oeiras, Portugal

    Jorge Carneiro

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© 2006 Springer-Verlag Berlin Heidelberg

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Pasek, R. (2006). Theoretical Basis of Novelty Detection in Time Series Using Negative Selection Algorithms. In: Bersini, H., Carneiro, J. (eds) Artificial Immune Systems. ICARIS 2006. Lecture Notes in Computer Science, vol 4163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11823940_29

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  • DOI: https://doi.org/10.1007/11823940_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37749-8

  • Online ISBN: 978-3-540-37751-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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