揭示时间序列异常检测中的“异常”：一种自监督三域解决方案
Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution
AI总结：
提出自监督三域异常检测器TriAD，通过时域、频域和残差域的对比学习及discord发现算法，在无需异常标签的情况下有效检测不同长度异常，并在严格评估指标下性能显著优于SOTA模型。
摘要：
时间序列异常检测（TSAD）面临的持续挑战，尤其是异常标签的稀缺以及异常长度和形状的变异性，促使人们需要更高效的解决方案。由于有限的异常标签阻碍了TSAD中传统的监督模型，人们引入了各种SOTA深度学习技术（如自监督学习）来解决这一问题。然而，它们在处理异常长度和形状的变化时遇到困难，限制了其对各种异常的适应性。此外，许多基准数据集存在异常过于明显的问题，甚至连随机函数都能检测出来。被称为点调整（PA）的不适定评估指标加剧了这一问题，这可能导致模型性能被夸大。在此背景下，我们提出了一种基于自监督学习的三域异常检测器（TriAD），它通过在三个数据域——时域、频域和残差域——建模特征来解决这些挑战，且不依赖异常标签。与传统的对比学习方法不同，TriAD采用域间和域内对比损失来学习正常数据的共同属性，并将其与异常区分开来。此外，我们的方法通过与discord发现算法集成，能够检测不同长度的异常。值得注意的是，本研究首次重新评估了TSAD中深度学习的潜力，同时使用了严格设计的数据集（即UCR Archive）和评估指标（即PA%K和affiliation）。通过在UCR数据集上的实验结果，TriAD在基于PA%K的F1分数上比SOTA深度学习模型提高了三倍，与SOTA discord发现算法相比准确率提高了50%。
英文摘要：
The ongoing challenges in time series anomaly detection (TSAD), notably the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more efficient solution. As limited anomaly labels hinder traditional supervised models in TSAD, various SOTA deep learning techniques, such as self-supervised learning, have been introduced to tackle this issue. However, they encounter difficulties handling variations in anomaly lengths and shapes, limiting their adaptability to diverse anomalies. Additionally, many benchmark datasets suffer from the problem of having explicit anomalies that even random functions can detect. This problem is exacerbated by ill-posed evaluation metrics, known as point adjustment (PA), which can result in inflated model performance. In this context, we propose a novel self-supervised learning based Tri-domain Anomaly Detector (TriAD), which addresses these challenges by modeling features across three data domains - temporal, frequency, and residual domains - without relying on anomaly labels. Unlike traditional contrastive learning methods, TriAD employs both inter-domain and intra-domain contrastive loss to learn common attributes among normal data and differentiate them from anomalies. Additionally, our approach can detect anomalies of varying lengths by integrating with a discord discovery algorithm. It is worth noting that this study is the first to reevaluate the deep learning potential in TSAD, utilizing both rigorously designed datasets (i.e., UCR Archive) and evaluation metrics (i.e., PA%K and affiliation). Through experimental results on the UCR dataset, TriAD achieves an impressive three-fold increase in PA%K based F1 scores over SOTA deep learning models, and 50% increase of accuracy as compared to SOTA discord discovery algorithms.
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。
