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Unsupervised outlier detection for time series by entropy and dynamic time warping

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Abstract

In the last decade, outlier detection for temporal data has received much attention from data mining and machine learning communities. While other works have addressed this problem by two-way approaches (similarity and clustering), we propose in this paper an embedded technique dealing with both methods simultaneously. We reformulate the task of outlier detection as a weighted clustering problem based on entropy and dynamic time warping for time series. The outliers are then detected by an optimization problem of a new proposed cost function adapted to this kind of data. Finally, we provide some experimental results for validating our proposal and comparing it with other methods of detection.

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Acknowledgements

We thank anonymous reviewers for their very useful comments and suggestions.

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Authors and Affiliations

  1. University of Lyon1-LIRIS, 43 Bd du 11 Novembre 1918, 69622, Villeurbanne, France

    Seif-Eddine Benkabou & Khalid Benabdeslem

  2. LIZEO Online Media Group, 69002, Lyon, France

    Seif-Eddine Benkabou & Bruno Canitia

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  1. Seif-Eddine Benkabou
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  2. Khalid Benabdeslem
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  3. Bruno Canitia
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Correspondence to Seif-Eddine Benkabou.

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Benkabou, SE., Benabdeslem, K. & Canitia, B. Unsupervised outlier detection for time series by entropy and dynamic time warping. Knowl Inf Syst 54, 463–486 (2018). https://doi.org/10.1007/s10115-017-1067-8

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  • DOI: https://doi.org/10.1007/s10115-017-1067-8

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