CATS: Contrastive learning for Anomaly detection in Time Series

Anomaly detection (AD) plays a critical role in a wide variety of big data applications, including cybersecurity, monitoring, and network systems. It consists in finding patterns in time series data that indicate unexpected events such as faults or defects. Traditional AD approaches, predominantly based on reconstruction techniques, often yield suboptimal performance, particularly when anomalies are present in the training set. Conversely, contrastive learning (CL) has shown significant performance in image processing tasks and is increasingly applied in time series data classification and forecasting. However, traditional CL frameworks are not well-adapted for time series AD due to two key challenges. First, AD is typically performed only on normal instances, and thus CL does not benefit from knowledge about anomalous instances. Second, the temporal nature of time series data is often neglected when computing time series similarity, thereby hindering the effective learning of time series representation.To overcome these limitations, we propose CATS, a novel approach that leverages a temporal similarity measure to learn time series representations. Moreover, through negative data augmentation, CATS generates a more realistic distribution of anomalies, which enables anomaly-informed CL. Extensive experiments conducted on six real-world datasets demonstrate that CATS outperforms existing AD methods. Our results highlight the efficacy of CATS in enhancing time series AD performance in big data environment across various application domains.