  The accumulation of time-series data and the absence of labels make
time-series Anomaly Detection (AD) a self-supervised deep learning task.
Single-normality-assumption-based methods, which reveal only a certain aspect
of the whole normality, are incapable of tasks involved with a large number of
anomalies. Specifically, Contrastive Learning (CL) methods distance negative
pairs, many of which consist of both normal samples, thus reducing the AD
performance. Existing multi-normality-assumption-based methods are usually
two-staged, firstly pre-training through certain tasks whose target may differ
from AD, limiting their performance. To overcome the shortcomings, a deep
Contrastive One-Class Anomaly detection method of time series (COCA) is
proposed by authors, following the normality assumptions of CL and one-class
classification. It treats the original and reconstructed representations as the
positive pair of negative-sample-free CL, namely "sequence contrast". Next,
invariance terms and variance terms compose a contrastive one-class loss
function in which the loss of the assumptions is optimized by invariance terms
simultaneously and the "hypersphere collapse" is prevented by variance terms.
In addition, extensive experiments on two real-world time-series datasets show
the superior performance of the proposed method achieves state-of-the-art.
