PaAno uses patch-based 1D CNN embeddings trained with triplet and pretext losses to achieve state-of-the-art time-series anomaly detection on the TSB-AD benchmark for both univariate and multivariate data.
Paul Boniol, Qinghua Liu, Mingyi Huang, Themis Palpanas, and John Paparrizos
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PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
PaAno uses patch-based 1D CNN embeddings trained with triplet and pretext losses to achieve state-of-the-art time-series anomaly detection on the TSB-AD benchmark for both univariate and multivariate data.