159 citations · 331 across the 6 of their papers we have counts for
4 papers · 1 filter
CEDL: Centre-Enhanced Discriminative Learning for Anomaly Detection
Zahra Zamanzadeh Darban, Qizhou Wang, Charu C. Aggarwal +3
Supervised anomaly detection methods perform well in identifying known anomalies that are well represented in the training set. However, they often struggle to generalise beyond th…
GenIAS: Generator for Instantiating Anomalies in time Series
Zahra Zamanzadeh Darban, Qizhou Wang, Geoffrey I. Webb +3
Synthetic anomaly injection is a recent and promising approach for time series anomaly detection (TSAD), but existing methods rely on ad hoc, hand-crafted strategies applied to raw…
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
Zahra Zamanzadeh Darban, Yiyuan Yang, Geoffrey I. Webb +4
In time series anomaly detection (TSAD), the scarcity of labeled data poses a challenge to the development of accurate models. Unsupervised domain adaptation (UDA) offers a solutio…
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection
Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan +2
One main challenge in time series anomaly detection (TSAD) is the lack of labelled data in many real-life scenarios. Most of the existing anomaly detection methods focus on learnin…