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arXiv Machine Learning By Dongchan Cho, Jiho Han, Juwon Hwang, Changhee Min, Minsang Kim, Namsoon Jung

Distilling Multi-Teacher Scoring Principles for Unsupervised Time Series Anomaly Detection

Read the original on arXiv Machine Learning →

The paper introduces AnoMix, a method that distills the complementary anomaly scoring principles of multiple unsupervised time‑series detectors into a single lightweight model. By normalizing each teacher’s score sequence and applying a multi‑teacher ranking‑distillation objective called CoRank, AnoMix learns a student detector that uses pairwise supervision derived from teacher consensus. Experiments on seven benchmark datasets show that the compact student (under 80k parameters) outperforms individual detectors and static ensembles, and a zero‑shot variant can even surpass large pretrained time‑series foundation models on unseen data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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