Publication Type
Journal Article
Version
acceptedVersion
Publication Date
10-2024
Abstract
Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality deeply and comprehensively by devising advanced neural network structures and new reconstruction/prediction learning objectives. However, their one-class learning process can be misled by latent anomalies in training data (i.e., anomaly contamination) under the unsupervised paradigm. Their learning process also lacks knowledge about the anomalies. Consequently, they often learn a biased, inaccurate normality boundary. To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty modeling-based calibration and native anomaly-based calibration. Specifically, our approach adaptively penalizes uncertain predictions to restrain irregular samples in anomaly contamination during optimization, while simultaneously encouraging confident predictions on regular samples to ensure effective normality learning. This largely alleviates the negative impact of anomaly contamination. Our approach also creates native anomaly examples via perturbation to simulate time series abnormal behaviors. Through discriminating these dummy anomalies, our one-class learning is further calibrated to form a more precise normality boundary. Extensive experiments on ten real-world datasets show that our model achieves substantial improvement over sixteen state-of-the-art contenders.
Keywords
Anomaly detection, one-class classification, time series, anomaly contamination, native anomalies
Discipline
Databases and Information Systems
Areas of Excellence
Digital transformation
Publication
IEEE Transactions on Knowledge and Data Engineering
Volume
36
Issue
11
First Page
5723
Last Page
5736
ISSN
1041-4347
Identifier
10.1109/TKDE.2024.3393996
Publisher
Institute of Electrical and Electronics Engineers
Citation
XU, Hongzuo; WANG, Yijie; JIAN, Songlei; LIAO, Qing; WANG, Yongjun; and PANG, Guansong.
Calibrated one-class classification for unsupervised time series anomaly detection. (2024). IEEE Transactions on Knowledge and Data Engineering. 36, (11), 5723-5736.
Available at: https://ink.library.smu.edu.sg/sis_research/9854
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TKDE.2024.3393996