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

CARLA is a novel two-stage self-supervised contrastive representation learning approach that effectively detects anomalies in time series data by learning discriminative representations that distinguish normal and anomalous patterns.