CALAD uses reconstruction-error-based channel relevance to construct contrastive samples in a transformer autoencoder framework, outperforming baselines on real-world multivariate time series anomaly detection tasks especially under distribution shift.
IEEE Robotics and Automa- tion Letters3, 1544–1551 (2018)
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CALAD: Channel-Aware contrastive Learning for multivariate time series Anomaly Detection
CALAD uses reconstruction-error-based channel relevance to construct contrastive samples in a transformer autoencoder framework, outperforming baselines on real-world multivariate time series anomaly detection tasks especially under distribution shift.