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Active learning can boost unsupervised anomaly detection performance by over 12% in complex time series data, tackling the challenge of subtle anomalies head-on.
Spot anomalies before they happen: FATE uses disagreement among time-series forecasting models to provide early warning signals without needing labeled anomaly data.
