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arXiv Machine Learning By Cristhian Moya-Mota, Ignacio Aguilera-Martos, Diego Garc\'ia-Gil, Juli\'an Luengo

LEAP: a Latent Embedding Anomaly Patch-Transformer for time-series anomaly detection

Read the original on arXiv Machine Learning →

LEAP is a new time‑series anomaly detection model that avoids reconstruction by using masked prediction and latent‑embedding evidence. It removes patches from a window, predicts them from remaining context, and scores prediction errors as anomaly evidence. The model incorporates three label‑free novelty signals—latent‑embedding novelty, local temporal contrast, and per‑channel novelty—and achieves strong performance on the TSB‑AD benchmark, with statistically significant gains from each component.

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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