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cs.LG2026
Distilling Multi-Teacher Scoring Principles for Unsupervised Time Series Anomaly Detection
Dongchan Cho, Jiho Han, Juwon Hwang +3
Time series anomalies take diverse forms, and individual detectors encode different anomaly scoring principles that rarely cover all anomaly types. Recent unsupervised time-series…
cs.LG2025
Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection
Dongchan Cho, Jiho Han, Keumyeong Kang +3
Real-world multivariate time series anomalies are rare and often unlabeled. Additionally, prevailing methods rely on increasingly complex architectures tuned to benchmarks, detecti…