CARLA: Self-supervised contrastive representation learning for time series anomaly detection

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 introduce a novel end-to-end self-supervised ContrAstive Representation Learning approach for time series Anomaly detection (CARLA). While existing contrastive learning methods assume that augmented time series windows are positive samples and temporally distant windows are negative samples, we argue that these assumptions are limited as augmentation of time series can transform them to negative samples, and a temporally distant window can represent a positive sample. Existing approaches to contrastive learning for time series have directly copied methods developed for image analysis. We argue that these methods do not transfer well. Instead, our contrastive approach leverages existing generic knowledge about time series anomalies and injects various types of anomalies as negative samples. Therefore, CARLA not only learns normal behaviour but also learns deviations indicating anomalies. It creates similar representations for temporally close windows and distinct ones for anomalies. Additionally, it leverages the information about representations' neighbours through a self-supervised approach to classify windows based on their nearest/furthest neighbours to further enhance the performance of anomaly detection. In extensive tests on seven major real-world TSAD datasets, CARLA shows superior performance (F1 and AU- PR) over state-of-the-art self-supervised, semi-supervised, and unsupervised TSAD methods for univariate time series and multivariate time series. Our research highlights the immense potential of contrastive representation learning in advancing the TSAD field, thus paving the way for novel applications and in-depth exploration.