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TOPOMA: Time-Series Orthogonal Projection Operator with Moving Average for Interpretable and Training-Free Anomaly Detection

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Advances in Knowledge Discovery and Data Mining (PAKDD 2024)

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Abstract

We present TOPOMA, a time-series orthogonal projection operator with moving average that can identify anomalous points for multivariate time-series, without requiring any labels nor training. Despite intensive research the problem has received, it remains challenging due to 1) scarcity of labels, 2) occurrence of non-stationarity in online streaming, and 3) trust issues posed by the black-box nature of deep learning models. We tackle these issues by avoiding training a complex model on historical data as in previous work, rather we track a moving average estimate of variable subspaces that can compute the deviation of each time step via orthogonal projection onto the subspace. Further, we propose to replace the popular yet less principled global thresholding function of anomaly scores used in previous work with an adaptive one that can bound the occurrence of anomalous events to a given small probability. Our algorithm is shown to compare favourably with deep learning methods while being transparent to interpret.

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Authors and Affiliations

  1. Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne, NE1 8ST, UK

    Shanfeng Hu

  2. Institute of Virtual Reality and Intelligent Systems, Hangzhou Normal University, Hangzhou, 311121, China

    Ying Huang

Authors
  1. Shanfeng Hu
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  2. Ying Huang
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Corresponding author

Correspondence to Shanfeng Hu .

Editor information

Editors and Affiliations

  1. Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  2. Microsoft Research Asia, Beijing, China

    Xing Xie

  3. National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  4. Duke University, Durham, NC, USA

    Jian Pei

  5. National Cheng Kung University, Tainan, Taiwan

    Jen-Wei Huang

  6. Silesian University of Technology, Gliwice, Poland

    Jerry Chun-Wei Lin

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Hu, S., Huang, Y. (2024). TOPOMA: Time-Series Orthogonal Projection Operator with Moving Average for Interpretable and Training-Free Anomaly Detection. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14645. Springer, Singapore. https://doi.org/10.1007/978-981-97-2242-6_5

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  • DOI: https://doi.org/10.1007/978-981-97-2242-6_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2241-9

  • Online ISBN: 978-981-97-2242-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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