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The ongoing challenges in time series anomaly detection (TSAD), notably thescarcity of anomaly labels and the variability in anomaly lengths and shapes,have led to the need for a more efficient solution. As limited anomaly labelshinder traditional supervised models in TSAD, various SOTA deep learningtechniques, such as self-supervised learning, have been introduced to tacklethis issue. However, they encounter difficulties handling variations in anomalylengths and shapes, limiting their adaptability to diverse anomalies.Additionally, many benchmark datasets suffer from the problem of havingexplicit anomalies that even random functions can detect. This problem isexacerbated by ill-posed evaluation metrics, known as point adjustment (PA),which can result in inflated model performance. In this context, we propose anovel self-supervised learning based Tri-domain Anomaly Detector (TriAD), whichaddresses 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 bothinter-domain and intra-domain contrastive loss to learn common attributes amongnormal data and differentiate them from anomalies. Additionally, our approachcan detect anomalies of varying lengths by integrating with a discord discoveryalgorithm. It is worth noting that this study is the first to reevaluate thedeep 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 impressivethree-fold increase in PA%K based F1 scores over SOTA deep learning models, and50% increase of accuracy as compared to SOTA discord discovery algorithms.

Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution