When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection    [arxiv] [code]
Dongmin Kim, Sunghyun Park, and Jaegul Choo
Association for the Advancement of Artificial Intelligence (AAAI), 2024 (23.75% acceptance rate).

Deep Imbalanced Time-series Forecasting via Local Discrepancy Density  [arxiv]
Junwoo Park, Jungsoo Lee, Youngin Cho, Woncheol Shin, Dongmin Kim, Jaegul Choo, and Edward Choi
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2023 (23.97% acceptance rate).

WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting  [arxiv] [paper] [code]
Youngin Cho,* Daejin Kim,* Dongmin Kim, Mohammad Azam Khan, and Jaegul Choo  (*: equal contributions)
Conference on Neural Information Processing Systems (NeurIPS), 2022 (25.6% acceptance rate). 

Residual Correction in Real-Time Traffic Forecasting  [arxiv] [paper]
Daejin Kim,* Youngin Cho,* Dongmin Kim, Cheonbok Park, and Jaegul Choo (*: equal contributions)
ACM International Conference on Information and Knowledge Management (CIKM), 2022.