4 papers
GT-PSSM: Unified Probabilistic Framework for Stochastic Dynamics Modeling and Dependency Learning in Multivariate Time Series Anomaly Detection
Wonmo Koo, Jaeyeong Lee, Taeseong Yoon +1
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 trainin…
Knowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes
Jaeyeong Lee, Taeseong Yoon, Wonmo Koo +1
Industrial processes often generate complex, interdependent time-series data from multiple sensors across multiple stages, forming complex dependencies among variables and process…
Function-Space Priors for Bayesian Neural ODEs with Application to Vessel Trajectory Prediction
Jaeyeong Lee, Wonmo Koo, Heeyoung Kim
Vessel trajectory prediction from Automatic Identification System (AIS) data is essential for maritime situational awareness, yet it remains challenging due to irregular sampling,…
AIS-Based Vessel Trajectory Prediction Using Memory-Augmented Neural Networks
Wonmo Koo, Sanha Chang, Heeyoung Kim
Accurate vessel trajectory prediction is essential for safe and efficient maritime operations, enabling collision avoidance and supporting route optimization. Although memory-augme…