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.

我们引入了一种自监督对比表示学习方法（CARLA）用于时间序列异常检测，这是一种创新的端到端自监督框架，精心开发，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 能够为时间序列窗口生成稳健的表示。它通过 1）学习时间上接近的窗口的相似表示和窗口及其等效异常窗口的不相似表示，以及 2）使用自监督方法根据表示空间中的最近 / 最远邻居对窗口的正常 / 异常表示进行分类，有效地解决了时间序列数据中异常检测的固有挑战。通过在 7 个标准实际时间序列异常检测基准数据集上进行广泛实验，CARLA 表现出比现有最先进结果更好的 F1 和 AU-PR。我们的研究突显了对比表示学习在推动时间序列异常检测领域的巨大潜力，为该领域的新型应用和深入探索铺平了道路。