Mainstream unsupervised anomaly detection algorithms often excel in academic
datasets, yet their real-world performance is restricted due to the controlled
experimental conditions involving clean training data. Addressing the challenge
of training with noise, a prevalent issue in practical anomaly detection, is
frequently overlooked. In a pioneering endeavor, this study delves into the
realm of label-level noise within sensory time-series anomaly detection (TSAD).
This paper presents a novel and practical end-to-end unsupervised TSAD when the
training data are contaminated with anomalies. The introduced approach, called
TSAD-C, is devoid of access to abnormality labels during the training phase.
TSAD-C encompasses three modules: a Decontaminator to rectify the abnormalities
(aka noise) present in the training data, a Variable Dependency Modeling module
to capture both long-term intra- and inter-variable dependencies within the
decontaminated data that can be considered as a surrogate of the pure normal
data, and an Anomaly Scoring module to detect anomalies. Our extensive
experiments conducted on three widely used physiological datasets conclusively
demonstrate that our approach surpasses existing methodologies, thus
establishing a new state-of-the-art performance in the field.

本研究介绍了一种无监督式的感知时间序列异常检测方法（TSAD-C），通过对带有异常的训练数据进行去噪、建立变量依赖模型和检测异常值等步骤，成功在三个广泛使用的生理数据集上超越现有方法，达到了领域中的最新技术水平。