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
Time-series anomaly detection often returns scores or intervals, while analysts need to understand the abnormal behavior and the evidence supporting it. We introduce SAGE (Speciali…
Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles
Hyeongwon Kang, Jinwoo Park, Seunghun Han +1
Detecting anomalies in time-series data is critical in domains such as industrial operations, finance, and cybersecurity, where early identification of abnormal patterns is essenti…
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…