Efficient anomaly detection and diagnosis in multivariate time-
series data is of great importance for modern industrial applications.
However, building a system that is able to quickly and accurately
pinpoint anomalous observations is a challenging problem. This is
due to the lack of anomaly labels, high data volatility and the de-
mands of ultra-low inference times in modern applications. Despite
the recent developments of deep learning approaches for anomaly
detection, only a few of them can address all of these challenges.
In this paper, we propose TranAD, a deep transformer network
based anomaly detection and diagnosis model which uses attention-
based sequence encoders to swiftly perform inference with the
knowledge of the broader temporal trends in the data. TranAD uses
focus score-based self-conditioning to enable robust multi-modal
feature extraction and adversarial training to gain stability. Addi-
tionally, model-agnostic meta learning (MAML) allows us to train
the model using limited data. Extensive empirical studies on six pub-
licly available datasets demonstrate that TranAD can outperform
state-of-the-art baseline methods in detection and diagnosis perfor-
mance with data and time-efficient training. Specifically, TranAD
increases F1 scores by up to 17%, reducing training times by up to
99% compared to the baselines.