Calibrated One-Class Classification for Unsupervised Time Series Anomaly Detection
National University of Defense Technology · Shenzhen Institute of Information Technology · +2 more institutions
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…
Citation impact
- FWCI
- 35.41
- Percentile
- 100%
- References
- 58
Authors
6- HXHongzuo XuCorresponding
- YWYijie Wang
National University of Defense Technology
- SJSonglei Jian
National University of Defense Technology
- QLQing Liao
Shenzhen Institute of Information Technology, Harbin Institute of Technology
- YWYongjun Wang
National University of Defense Technology
Topics & keywords
- Computer science
- Anomaly detection
- Series (stratigraphy)
- Time series
- Class (philosophy)
- Artificial intelligence
- Pattern recognition (psychology)
- Data mining
- Climate action