RoCA：面向污染数据的鲁棒对比单类时间序列异常检测
RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data
AI总结：
针对时间序列异常检测中现有正态性假设方法的三大局限，提出融合单类分类与对比学习、基于异常分数识别潜在异常定义边界的RoCA方法，在多类数据集上性能显著提升。
摘要：
时间序列信号的累积与标签的缺失使得时间序列异常检测（AD）成为一项深度学习自监督任务。基于正态性假设的方法存在以下三方面局限：（1）单一假设难以刻画完整的正态性，或会产生一定偏差；（2）部分假设可能违背异常检测的基本原则；（3）这类方法的基本假设是训练数据未受污染（无异常），这在实际场景中并不现实，会导致鲁棒性下降。\n本文提出了一种新型鲁棒方法RoCA，据我们所知，这是首个同时解决上述三大挑战的方法。该方法将单类分类与对比学习的独立假设融合到同一训练流程中，以刻画更完整的所谓“正态性”。此外，它会在整个训练过程中监控训练数据，并计算一种精心设计的异常分数。受离群点暴露概念的启发，该分数有助于识别潜在异常，这些异常随后被用于定义分类边界。\n在AIOps数据集上，与未考虑污染的COCA方法相比，性能提升了6%。在两个大规模高维多变量数据集上，性能提升了5%至10%。RoCA在单变量和多变量数据集上均取得了最高的平均性能。源代码可在https://github.com/ruiking04/RoCA 获取。
英文摘要：
The accumulation of time-series signals and the absence of labels make time-series Anomaly Detection (AD) a self-supervised task of deep learning. Methods based on normality assumptions face the following three limitations: (1) A single assumption could hardly characterize the whole normality or lead to some deviation. (2) Some assumptions may go against the principle of AD. (3) Their basic assumption is that the training data is uncontaminated (free of anomalies), which is unrealistic in practice, leading to a decline in robustness. This paper proposes a novel robust approach, RoCA, which is the first to address all of the above three challenges, as far as we are aware. It fuses the separated assumptions of one-class classification and contrastive learning in a single training process to characterize a more complete so-called normality. Additionally, it monitors the training data and computes a carefully designed anomaly score throughout the training process. This score helps identify latent anomalies, which are then used to define the classification boundary, inspired by the concept of outlier exposure. The performance on AIOps datasets improved by 6% compared to when contamination was not considered (COCA). On two large and high-dimensional multivariate datasets, the performance increased by 5% to 10%. RoCA achieves the highest average performance on both univariate and multivariate datasets. The source code is available at https://github.com/ruiking04/RoCA.
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。
