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International Work-Conference on Artificial Neural Networks

IWANN 2017: Advances in Computational Intelligence pp 116–128Cite as

Multi-resolution Time Series Discord Discovery

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10306))

Abstract

Discord Discovery is a recent approach for anomaly detection in time series that has attracted much research because of the wide variety of real-world applications in monitoring systems. However, finding anomalies by different levels of resolution has received little attention in this research line. In this paper, we introduce a multi-resolution representation based on local trends and mean values of the time series. We require the level of resolution as parameter, but it can be automatically computed if we consider the maximum resolution of the time series. In order to provide a useful representation for discord discovery, we propose dissimilarity measures for achieving high effective results, and a symbolic representation based on SAX technique for efficient searches using a multi-resolution indexing scheme. We evaluate our method over a diversity of data domains achieving a better performance compared with some of the best-known classic techniques.

H. Sanchez—Work supported by a research grant from CONICYT-Chile.

B. Bustos—Supported by the Millennium Nucleus Center for Semantic Web Research under Grant NC120004.

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Authors and Affiliations

  1. Department of Computer Science, University of Chile, Santiago, Chile

    Heider Sanchez & Benjamin Bustos

Authors
  1. Heider Sanchez
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  2. Benjamin Bustos
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Corresponding author

Correspondence to Benjamin Bustos .

Editor information

Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Barcelona, Spain

    Andreu Catala

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Sanchez, H., Bustos, B. (2017). Multi-resolution Time Series Discord Discovery. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_11

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  • DOI: https://doi.org/10.1007/978-3-319-59147-6_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59146-9

  • Online ISBN: 978-3-319-59147-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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