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Found 4 papers, 3 papers with code
PATE: Proximity-Aware Time series anomaly Evaluation
We introduce Proximity-Aware Time series anomaly Evaluation (PATE), a novel evaluation metric that incorporates the temporal relationship between prediction and anomaly intervals.
RESTAD: REconstruction and Similarity based Transformer for time series Anomaly Detection
This layer fits a non-parametric density in the latent representation, such that a high RBF output indicates similarity with predominantly normal training data.
Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification
This paper introduces a two-stage framework leveraging representation learning and personalization to improve anomaly detection performance in PPG data.
Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability
Unfortunately, there is high inter-subject variability in the SSL-learned representations, which makes working with this data more challenging when labeled data is scarce.
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