Author

W. Koo

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Conference

Probabilistic Adaptive Graph Transformer for Uncertainty-Aware Multivariate Time Series Anomaly Detection

Multivariate time series anomaly detection (MTAD) is important for ensuring reliable operation and improving service quality in industrial systems. Forecasting-based methods have been a primary approach for MTAD, detecting anomalies by assuming that anomalous data points produce higher forecasting errors than normal on...

W. Koo, Heeyoung Kim · IISE Annual Conference &... · 0 citations

#machine learning Preprint Sep 2026

GT-PSSM: Unified Probabilistic Framework for Stochastic Dynamics Modeling and Dependency Learning in Multivariate Time Series Anomaly Detection

Multivariate time series anomaly detection (MTAD) is crucial for ensuring the safe and reliable operation of complex systems. Many existing methods learn normal patterns by training reconstruction or forecasting models on predominantly normal data. However, a large portion of these approaches rely on deterministic mode...

W. Koo, Jaeyeong Lee, Taeseong Yoon et al. · 0 citations