5 papers
A Simple State Space Model Excels at Multivariate Time Series Classification
Hassan Saadatmand, Geoffrey I. Webb, Hamid Rezatofighi +1
Structured state space models (SSMs) have recently emerged as a promising foundation for sequence modeling, with Mamba-based architectures demonstrating strong performance through…
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…
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…
EEG-X: Device-Agnostic and Noise-Robust Foundation Model for EEG
Navid Mohammadi Foumani, Soheila Ghane, Nam Nguyen +3
Foundation models for EEG analysis are still in their infancy, limited by two key challenges: (1) variability across datasets caused by differences in recording devices and configu…
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…