  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 Long-range 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 from all types. Our extensive experiments conducted on three reliable
datasets conclusively demonstrate that our approach surpasses existing
methodologies, thus establishing a new state-of-the-art performance in the
field.
