Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution.
CoRR(2023)
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.
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