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2022

Multivariate Time Series Anomaly Detection with Few Positive Samples

Xue, Feng, Yan, Weizhong

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Given the scarcity of anomalies in real-world applications, the majority of literature has been focusing on modeling normality.

The learned representations enable anomaly detection as the normality model is trained to capture certain key underlying data regularities under normal circumstances.
In practical settings, particularly industrial time series anomaly detection, we often encounter situations where a large amount of normal operation data is available along with a small number of anomaly events collected over time.
This practical situation calls for methodologies to leverage these small number of anomaly events to create a better anomaly detector.

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