3 papers
cs.LG2026
PaP-NF: Probabilistic Long-Term Time Series Forecasting via Prefix-as-Prompt Reprogramming and Normalizing Flows
Minju Kim, Youngbum Hur
Time series forecasting plays a central role in many real-world applications and has been extensively studied. Most existing approaches rely on deterministic models. However, real-…
cs.LG2026
CALAD: Channel-Aware contrastive Learning for multivariate time series Anomaly Detection
Jaehyeop Hong, Youngbum Hur
Multivariate time series anomaly detection has become increasingly important in real-world applications, where labeled data are often scarce. Many existing approaches rely on unsup…
cs.CV2026
CoReVAD: A Contextual Reasoning Framework for Training-Free Video Anomaly Detection
Hyeongmuk Lim, Youngbum Hur
Existing Video Anomaly Detection (VAD) methods typically rely on task-specific training, leading to strong domain dependency and high training costs. Moreover, most existing method…