CL-TAD: A Contrastive-Learning-Based Method for Time Series Anomaly Detection

Abstract

Anomaly detection has gained increasing attention in recent years, but detecting anomalies in time series data remains challenging due to temporal dynamics, label scarcity, and data diversity in real-world applications. To address these challenges, we introduce a novel method for anomaly detection in time series data, called CL-TAD (Contrastive-Learning-based method for Times series Anomaly Detection), which employs a contrastive-learning-based representation learning technique. Inspired by the successes of reconstruction-based approaches and contrastive learning approaches, the proposed method seeks to leverage these approaches for time series anomaly detection. The CL-TAD method is comprised of two main components: positive sample generation and contrastive-learning-based representation learning. The former component generates positive samples by trying to reconstruct the original data from masked samples. These positive samples, in conjunction with the original data, serve as input for the contrastive-learning-based representation learning component. The representations of input original data and their masked data are used to detect anomalies later on. Experimental results have demonstrated that the CL-TAD method achieved the best performance on five datasets out of nine benchmark datasets over 10 other recent methods. By leveraging the reconstruction learning and contrastive learning techniques, our method offers a promising solution for effectively detecting anomalies in time series data by handling the issues raised by label scarcity and data diversity, delivering high performance.