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Ditch likelihood-based anomaly detection: this method maps time-series data to a latent space where anomalies are defined as violations of prescribed temporal dynamics, enabling more robust detection.
Forget disjoint anomaly detection and imputation pipelines: CINDI uses conditional normalizing flows to jointly model and restore corrupted time series data with state-of-the-art results.
