Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders

arXiv:2405.15273v1 Announce Type: new
Abstract: Time series anomaly detection plays a vital role in a wide range of applications. Existing methods require training one specific model for each dataset, which exhibits limited generalization capability across different target datasets, hindering anomaly detection performance in various scenarios with scarce training data. Aiming at this problem, we propose constructing a general time series anomaly detection model, which is pre-trained on extensive multi-domain datasets and can subsequently apply to a multitude of downstream scenarios. …

abstract adversarial anomaly anomaly detection applications arxiv bottlenecks capability cs.lg dataset datasets detection general performance role series time series training type vital