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Distance-Based Outliers in Sequences

  • Conference paper
Distributed Computing and Internet Technology (ICDCIT 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3816))

Abstract

Automatically finding interesting, novel or surprising patterns in time series data is useful in several applications, such as fault diagnosis and fraud detection. In this paper, we extend the notion of distance-based outliers to time series data and propose two algorithms to detect both global and local outliers in time series data. We illustrate these algorithms on some real datasets.

Keywords: Novelty detection, Outlier detection, Time series, Sequence mining.

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© 2005 Springer-Verlag Berlin Heidelberg

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Palshikar, G.K. (2005). Distance-Based Outliers in Sequences. In: Chakraborty, G. (eds) Distributed Computing and Internet Technology. ICDCIT 2005. Lecture Notes in Computer Science, vol 3816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11604655_61

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  • DOI: https://doi.org/10.1007/11604655_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30999-4

  • Online ISBN: 978-3-540-32429-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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