RedLamp detects time series anomalies by training a multiclass classifier on 11 types of augmented pseudo-anomalies, combining its predictions with reconstruction error, and using soft labels to resist contamination.
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Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomalies
RedLamp detects time series anomalies by training a multiclass classifier on 11 types of augmented pseudo-anomalies, combining its predictions with reconstruction error, and using soft labels to resist contamination.