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TSLOD: a coupled generalized subsequence local outlier detection model for multivariate time series

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Abstract

Unsupervised subsequence outlier detection on multivariate time series (MTS) is a valuable problem in practice that can observably save the cost of labeling and provide interpretability in real applications. For the task, most of the classic methods are under two strong assumptions: (i) stationary MTS. it may have difficulty coping with the phenomenon of time drift. (ii) Attribute-level IIDness (independent and identically distributed), it may ignore the relationship between attribute when measuring the similarity between multivariate subsequences. The above assumptions limit the availability of existing methods in real scenarios. To address this issue, this paper introduces a novel coupled generalized local outlier detection model for MTS, which extends the traditional generalized local outlier detection model to cope with subsequence outlier detection tasks by incorporating a novel Non-IID similarity metric. Specifically, the proposed method mainly includes three aspects: (i) represents the MTS relationship in symbolic space which provides a lower complexity and satisfactory sensitivity. (ii) Proposes a Non-IID coupled similarity metric (TSDis) which considers the intrinsic intra-attribute and inter-attribute coupling between segments. (iii) Extends the traditional generalized local outlier detection model to handle subsequence outlier detection tasks by embedding Non-IID coupled similarity metric. Experimental results show the proposed method can utilize the potential characteristics of MTS effectively and stably. Meanwhile, it detects outliers more accurately than baseline approaches on 12 time-series datasets.

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Notes

  1. Subsequence is an exchangeable term for segment in this paper.

  2. Attribute are also an interchangeable term for variable in the context of relational data.

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Acknowledgements

This research has been supported in part by the Science and Technology Program of State Grid Corporation of China (SGJS0000DKJS2000952), by the Special Fundation of Jiangsu Provincial Industry and Information Transformation and Upgrading in 2020 (Research on key core technologies of artificial intelligence algorithm frameworks, tools and platforms), by the National Natural Science Foundation of China under Grant (61806096).

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Authors and Affiliations

  1. State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China

    Fan Meng & Yang Gao

  2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Huihui Wang

  3. State Grid Jiangsu Corp., Nanjing, China

    Yi Liu

  4. China Science Dawn Nanjing Research Institute Co., Ltd, Nanjing, China

    Hairong Wang

  5. Department of Computer Science and Technology, Nanjing University, Nanjing, China

    Yang Gao

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  1. Fan Meng
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Correspondence to Yang Gao.

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Meng, F., Gao, Y., Wang, H. et al. TSLOD: a coupled generalized subsequence local outlier detection model for multivariate time series. Int. J. Mach. Learn. & Cyber. 13, 1493–1504 (2022). https://doi.org/10.1007/s13042-021-01462-x

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