CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection

arXiv:2308.09296v3 Announce Type: replace
Abstract: One main challenge in time series anomaly detection (TSAD) is the lack of labelled data in many real-life scenarios. Most of the existing anomaly detection methods focus on learning the normal behaviour of unlabelled time series in an unsupervised manner. The normal boundary is often defined tightly, resulting in slight deviations being classified as anomalies, consequently leading to a high false positive rate and a limited ability to generalise normal patterns. To address this, we …

abstract anomaly anomaly detection arxiv challenge cs.lg cs.ne data detection detection methods focus life normal representation representation learning series time series type unsupervised