5 papers
Not All Variables Agree: Reliability-Aware Variable-Wise Gradient Surgery for Multivariate Time-Series Forecasting
Jinwoo Park, Hyeongwon Kang, Pilsung Kang
In data-driven training, multivariate time-series forecasting is usually optimized with a scalar loss averaged over samples, variables, and horizons. This averaging is convenient,…
Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning
Seung Hun Han, Hyeongwon Kang, Jinwoo Park +1
Despite the increasing sophistication of industrial AI systems, the ability to reliably detect subtle and noisy anomalies in complex time series data remains a critical yet unresol…
Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers
Hyeongwon Kang, Jeongseob Kim, Jinwoo Park +1
Recent studies have explored large language models for time-series anomaly detection, yet existing approaches often rely on a single general-purpose model to directly infer anomaly…
COMET: Codebook-based Online-adaptive Multi-scale Embedding for Time-series Anomaly Detection
Jinwoo Park, Hyeongwon Kang, Seung Hun Han +1
Time series anomaly detection is a critical task across various industrial domains. However, capturing temporal dependencies and multivariate correlations within patch-level repres…
RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM considering Token-level information
Gunho No, Yukyung Lee, Hyeongwon Kang +1
As the IT industry advances, system log data becomes increasingly crucial. Many computer systems rely on log texts for management due to restricted access to source code. The need…