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 Transactions on Fuzzy Systems23, 688–700 (2014) CALAD 15
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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.