4 citations · 6 across the 14 of their papers we have counts for
5 papers · 1 filter
CANDI: Curated Test-Time Adaptation for Multivariate Time-Series Anomaly Detection Under Distribution Shift
HyunGi Kim, Jisoo Mok, Hyungyu Lee +2
Multivariate time-series anomaly detection (MTSAD) aims to identify deviations from normality in multivariate time-series and is critical in real-world applications. However, in re…
Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detection
HyunGi Kim, Jisoo Mok, Dongjun Lee +3
Utilizing the complex inter-variable causal relationships within multivariate time-series provides a promising avenue toward more robust and reliable multivariate time-series anoma…
Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation
HyunGi Kim, Siwon Kim, Jisoo Mok +1
Deep Neural Networks have spearheaded remarkable advancements in time series forecasting (TSF), one of the major tasks in time series modeling. Nonetheless, the non-stationarity of…
A Comprehensive Survey of Deep Learning for Time Series Forecasting: Architectural Diversity and Open Challenges
Jongseon Kim, Hyungjoon Kim, HyunGi Kim +2
Time series forecasting is a critical task that provides key information for decision-making. After traditional statistical and machine learning approaches, various fundamental dee…
Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation
Kukjin Choi, Jihun Yi, Jisoo Mok +1
Continuous efforts are being made to advance anomaly detection in various manufacturing processes to increase the productivity and safety of industrial sites. Deep learning replace…