CARLA：用于时间序列异常检测的自监督对比表示学习
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection
AI总结：
CARLA 提出一种自监督对比表示学习方法，通过注入异常作为负样本并利用邻居信息，在七个真实时间序列异常检测数据集上优于现有方法。
摘要：
时间序列异常检测（TSAD）面临的一个主要挑战是许多现实场景中缺乏标注数据。现有的大多数异常检测方法侧重于以无监督方式学习未标注时间序列的正常行为。正常边界通常被定义得较为严格，导致轻微偏差被分类为异常，从而造成较高的假阳性率，并且泛化正常模式的能力有限。为了解决这一问题，我们提出了一种新颖的端到端自监督对比表示学习方法，用于时间序列异常检测（CARLA）。现有的对比学习方法假设增强后的时间序列窗口是正样本，而时间上相距较远的窗口是负样本，我们认为这些假设存在局限性，因为对时间序列进行增强可能将其转变为负样本，而时间上相距较远的窗口也可能代表正样本。我们的对比方法利用关于时间序列异常的现有通用知识，并注入多种类型的异常作为负样本。因此，CARLA 不仅学习正常行为，还学习指示异常的偏差。它为时间上相近的窗口创建相似的表示，并为异常创建不同的表示。此外，它通过自监督方法利用表示邻居的信息，根据窗口的最近/最远邻居对窗口进行分类，以进一步增强异常检测的性能。在七个主要真实世界时间序列异常检测数据集上的广泛测试中，CARLA 表现出优于最先进的自监督和无监督 TSAD 方法的性能。我们的研究表明了对比表示学习在推进时间序列异常检测方面的潜力。
英文摘要：
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. 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 closed 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 time series anomaly detection datasets, CARLA shows superior performance over state-of-the-art self-supervised and unsupervised TSAD methods. Our research shows the potential of contrastive representation learning to advance time series anomaly detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
