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Parameter-Free Search of Time-Series Discord

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Abstract

Time-series discord is widely used in data mining applications to characterize anomalous subsequences in time series. Compared to some other discord search algorithms, the direct search algorithm based on the recurrence plot shows the advantage of being fast and parameter free. The direct search algorithm, however, relies on quasi-periodicity in input time series, an assumption that limits the algorithm's applicability. In this paper, we eliminate the periodicity assumption from the direct search algorithm by proposing a reference function for subsequences and a new sampling strategy based on the reference function. These measures result in a new algorithm with improved efficiency and robustness, as evidenced by our empirical evaluation.

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Authors and Affiliations

  1. School of Information Technology, Deakin University, Geelong, VIC 3220, Australia

    Wei Luo

  2. School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD 4072, Australia

    Marcus Gallagher (Member, IEEE) & Janet Wiles (Member, IEEE)

Authors
  1. Wei Luo
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  2. Marcus Gallagher
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  3. Janet Wiles
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Corresponding author

Correspondence to Wei Luo.

Additional information

Support by Australian Research Council Linkage Grant No. LP 0776417.

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Luo, W., Gallagher, M. & Wiles, J. Parameter-Free Search of Time-Series Discord. J. Comput. Sci. Technol. 28, 300–310 (2013). https://doi.org/10.1007/s11390-013-1330-8

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  • DOI: https://doi.org/10.1007/s11390-013-1330-8

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