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Developing an unsupervised real-time anomaly detection scheme for time series with multi-seasonality
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Wu, Wentai, He, Ligang, Lin, Weiwei, Su, Yi, Cui, Yuhua, Maple, Carsten and Jarvis, Stephen A. (2022) Developing an unsupervised real-time anomaly detection scheme for time series with multi-seasonality. IEEE Transactions on Knowledge and Data Engineering, 34 (9). pp. 4147-4160. doi:10.1109/TKDE.2020.3035685 ISSN 1041-4347.
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Official URL: http://dx.doi.org/10.1109/TKDE.2020.3035685
Abstract
On-line detection of anomalies in time series is a key technique used in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources and the variety of demands make this task more challenging than ever. Firstly, the rapid increase in unlabeled data means supervised learning is becoming less suitable in many cases. Secondly, a large portion of time series data have complex seasonality features. Thirdly, on-line anomaly detection needs to be fast and reliable. In light of this, we have developed a prediction-driven, unsupervised anomaly detection scheme, which adopts a backbone model combining the decomposition and the inference of time series data. Further, we propose a novel metric, Local Trend Inconsistency (LTI), and an efficient detection algorithm that computes LTI in a real-time manner and scores each data point robustly in terms of its probability of being anomalous. We have conducted extensive experimentation to evaluate our algorithm with several datasets from both public repositories and production environments. The experimental results show that our scheme outperforms existing representative anomaly detection algorithms in terms of the commonly used metric, Area Under Curve (AUC), while achieving the desired efficiency.
Item Type: | Journal Article | ||||||||||||||||||||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||||||||||||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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Library of Congress Subject Headings (LCSH): | Time-series analysis, Anomaly detection (Computer security) | ||||||||||||||||||||||||||||||
Journal or Publication Title: | IEEE Transactions on Knowledge and Data Engineering | ||||||||||||||||||||||||||||||
Publisher: | IEEE Computer Society | ||||||||||||||||||||||||||||||
ISSN: | 1041-4347 | ||||||||||||||||||||||||||||||
Official Date: | 1 September 2022 | ||||||||||||||||||||||||||||||
Dates: |
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Volume: | 34 | ||||||||||||||||||||||||||||||
Number: | 9 | ||||||||||||||||||||||||||||||
Page Range: | pp. 4147-4160 | ||||||||||||||||||||||||||||||
DOI: | 10.1109/TKDE.2020.3035685 | ||||||||||||||||||||||||||||||
Status: | Peer Reviewed | ||||||||||||||||||||||||||||||
Publication Status: | Published | ||||||||||||||||||||||||||||||
Reuse Statement (publisher, data, author rights): | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||||||||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||||||||||||||||||||||
Date of first compliant deposit: | 10 November 2020 | ||||||||||||||||||||||||||||||
Date of first compliant Open Access: | 10 November 2020 | ||||||||||||||||||||||||||||||
RIOXX Funder/Project Grant: |
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