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A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives

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Advances in Information and Communication (FICC 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1363))

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Abstract

Anomaly detection is a significant problem that has been studied in a broader spectrum of research areas due to its diverse applications in different domains. Despite the usage of modern technologies and the advances in system monitoring and anomaly detection techniques, false-positive rates are still high. There exist many anomaly detection algorithms, among them few are domain-specific, and others are more generic techniques. Despite a significant amount of advance in this research area, there does not exist a single winning anomaly detector known to work well across different datasets. In this paper, we review the literature related to types of anomalies, data types of anomalies, data types of time-series, components of time-series data, classification of anomalies context, and classification methods of time-series anomalies detection. We presented a taxonomy to characterize the various aspects related to time-series anomaly detection. One of the key challenges in current anomaly detection techniques is to perform anomaly detection with regards to the type of activities or the context that a system is exposed. We hope that this investigation gives a more remarkable ability to understand the evolving methods of time-series anomaly detection and how computational methods can be applied in this domain in the future.

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Author information

Authors and Affiliations

  1. The University of Newcastle, Newcastle, 2308, Australia

    Kamran Shaukat & Suhuai Luo

  2. University of Engineering and Technology, Lahore, 54890, Pakistan

    Talha Mahboob Alam, Shakir Shabbir, Syed Konain Abbas & Umair Javed

  3. Norwegian University of Science and Technology, 7491, Trondheim, Norway

    Ibrahim A. Hameed

  4. Data61, Commonwealth Scientific and Industrial Research Organisation, Canberra, Australia

    Jiaming Li

Authors
  1. Kamran Shaukat
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  2. Talha Mahboob Alam
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  3. Suhuai Luo
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  4. Shakir Shabbir
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  5. Ibrahim A. Hameed
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  6. Jiaming Li
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  7. Syed Konain Abbas
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  8. Umair Javed
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Corresponding author

Correspondence to Kamran Shaukat .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Shaukat, K. et al. (2021). A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives. In: Arai, K. (eds) Advances in Information and Communication. FICC 2021. Advances in Intelligent Systems and Computing, vol 1363. Springer, Cham. https://doi.org/10.1007/978-3-030-73100-7_60

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