We introduce a Self-supervised Contrastive Representation Learning Approach for Time Series Anomaly Detection (CARLA), an innovative end-to-end self-supervised framework carefully developed to identify anomalous patterns in both univariate and multivariate time series data. By taking advantage of contrastive representation learning, We introduce an innovative end-to-end self-supervised deep learning framework carefully developed to identify anomalous patterns in both univariate and multivariate time series data. By taking advantage of contrastive representation learning, CARLA effectively generates robust representations for time series windows. It achieves this by 1) learning similar representations for temporally close windows and dissimilar representations for windows and their equivalent anomalous windows and 2) employing a self-supervised approach to classify normal/anomalous representations of windows based on their nearest/furthest neighbours in the representation space. Most of the existing models focus on learning normal behaviour. The normal boundary is often tightly defined, which can result in slight deviations being classified as anomalies, resulting in a high false positive rate and limited ability to generalise normal patterns. CARLA's contrastive learning methodology promotes the production of highly consistent and discriminative predictions, thereby empowering us to adeptly address the inherent challenges associated with anomaly detection in time series data. Through extensive experimentation on 7 standard real-world time series anomaly detection benchmark datasets, CARLA demonstrates F1 and AU-PR superior to existing state-of-the-art results. Our research highlights the immense potential of contrastive representation learning in advancing the field of time series anomaly detection, thus paving the way for novel applications and in-depth exploration in this domain.

翻译：我们提出一种面向时间序列异常检测的自监督对比表示学习方法（CARLA），这是一个精心设计的创新端到端自监督框架，用于识别单变量和多变量时间序列数据中的异常模式。通过利用对比表示学习，CARLA能够有效生成时间序列窗口的鲁棒表示。其实现方式包括：1）学习时间相近窗口的相似表示，以及窗口与其等价异常窗口的相异表示；2）采用自监督方法，基于表示空间中最近/最远邻域分类窗口的正常/异常表示。现有模型大多聚焦于学习正常行为，其正常边界定义通常较为严格，导致微小偏差即被归类为异常，从而引发高误报率且泛化正常模式的能力有限。CARLA的对比学习方法促进了高度一致且具判别性的预测生成，使我们能够灵活应对时间序列异常检测的内在挑战。在7个标准真实世界时间序列异常检测基准数据集上的广泛实验表明，CARLA在F1分数和AU-PR指标上均优于现有最优结果。本研究凸显了对比表示学习在推动时间序列异常检测领域发展中的巨大潜力，为该领域的新应用与深度探索铺平了道路。