Papers
1–3 of 3Research Paper·Mar 13, 2026
Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection
Multivariate time series anomalies often manifest as shifts in cross-channel dependencies rather than simple amplitude excursions. In autonomous driving, for instance, a steering command might be inte...
8.0 viability
Research Paper·Mar 6, 2026
DQE: A Semantic-Aware Evaluation Metric for Time Series Anomaly Detection
Time series anomaly detection has achieved remarkable progress in recent years. However, evaluation practices have received comparatively less attention, despite their critical importance. Existing me...
7.0 viability
Research Paper·Mar 10, 2026
GNNs for Time Series Anomaly Detection: An Open-Source Framework and a Critical Evaluation
There is growing interest in applying graph-based methods to Time Series Anomaly Detection (TSAD), particularly Graph Neural Networks (GNNs), as they naturally model dependencies among multivariate si...
5.0 viability