Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection

arXiv:2207.12201v2 Announce Type: replace
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, …

abstract advanced anomaly anomaly detection arxiv availability class classification cs.ai cs.lg current data detection domains however line network neural network normality prediction process research series time series type unsupervised work