Online Time Series Forecasting

Latest papers

11 papers3 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

Guarded Agent-in-the-Loop Residual Adaptation for Evolving Wind Power Regimes
Oct 26, 2026Yuhang Dong, Min Xia, Jinghong Li +3Wind Power Forecasting Non-Stationary Time Series Forecasting
Nanjing University of Information Science and Technology, Nanjing, China
Local and Global Recursive Ridge Regression for Online Residual Correction of Frozen Multivariate Forecasters under Label Delay
Oct 21, 2026Takumi Fujimoto, Hiroaki NishiMultivariate Time Series Forecasting Online Time Series Forecasting
School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, Japan · Department of System Design, Faculty of Science and Technology, Keio University, Japan
Look Back, Correct Forward: Error Feedback Calibration for Time Series Forecasting
Oct 16, 2026Haonan Yang, Yulin He, Zhuo LiTime Series Forecasting Post-Hoc Calibration
College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China.
EPOC: Endpoint-Preserving Online Correction With Compressed Residual State for Multi-Horizon Time Series Forecasting
Sep 25, 2026Takumi Fujimoto, Hiroaki NishiOnline Time Series Forecasting Multivariate Time Series Forecasting
School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan · Department of System Design, Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan
When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting
Sep 21, 2026Yifan Hu, Xilin Dai, Zhiyuan Qu +4Online Time Series Forecasting Time Series Forecasting
Ant International · Tsinghua University · The Chinese University of Hong Kong
LoRA-RC: Reservoir Computing with Low-Rank Adaptation
Sep 11, 2026Wenbin WanOnline Time Series Forecasting Dynamical Systems
Department of Mechanical Engineering, University of New Mexico, Albuquerque, NM 87131, USA
Compositional Spectral Prompts for LLM-based Online Time Series Forecasting
Sep 2, 2026Seungyoon Choi, Hyunchul Kim, Jae-Gil Lee +1Online Time Series Forecasting Time Series Forecasting
KAIST Daejeon, Republic of Korea
EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts
Jun 3, 2026Yiming Lu, Sihang Zeng, Zhengxu Tang +3Online Time Series Forecasting Epidemic Forecasting
Emory University · University of Washington
Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration
May 27, 2026Haonan Wen, Hanyang Chen, Songhe FengIrregular Time-Series Modeling Online Time Series Forecasting
Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education School of Computer Science and Technology, Beijing Jiaotong University Beijing, China · School of Computer Science and Technology, Beijing Jiaotong University Beijing, China · Tangshan Research Institute, Beijing Jiaotong University Tangshan, China
Fast Training of Mixture-of-Experts for Time Series Forecasting via Expert Loss Integration
May 11, 2026Btissame El Mahtout, Florian ZielOnline Time Series Forecasting Time Series Forecasting
Chair of Data Science in Energy and Environment, University of Duisburg-Essen, Germany · Chair of Econometrics and Statistics, Technical University of Dortmund, Germany
Dynamic Multi-period Experts for Online Time Series Forecasting
Mar 10, 2026Seungha Hong, Sukang Chae, Suyeon Kim +2Online Time Series Forecasting Time Series Forecasting
Pohang University of Science and Technology, Pohang, Republic of Korea · Kyungpook National University, Daegu, Republic of Korea

Oct 26, 2026J/K move · Enter open · S save

Guarded Agent-in-the-Loop Residual Adaptation for Evolving Wind Power Regimes

Yuhang Dong, Min Xia, Jinghong Li, Yue Dai+2

Nanjing University of Information Science and Technology, Nanjing, China

Wind Power Forecasting Non-Stationary Time Series Forecasting Agentic Time Series Forecasting Online Time Series Forecasting Residual Learning

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Wind power forecasting models are often deployed as fixed temporal backbones, but the errors they make can vary across meteorological regimes such as cold waves, heat-and-calm periods, productive typhoon, and destructive storms. We present a guarded Agent-in-the-loop residual adaptation framework that leaves the temporal backbone unchanged and adapts only a small set of scene-conditioned residual experts. An Agent layer interprets external scene information, identifies the current regime, selects residual experts, and applies a correction only when recent observed errors support it. New experts enter the pool after they outperform their initializations on a validation set, keeping model growth under explicit control. Backbone-only and Agent-enhanced forecasts are compared at lead times of 4, 8, 12, 24, and 48 across nine time-series backbones. Across these runs, the guarded framework lowers both MAE and RMSE for all 45 backbone-horizon combinations, with average MAE reductions ranging from 11.21% to 20.26% across backbones. Online-selection baselines, component ablations, and routing analyses identify the roles of residual experts, scene-aware routing, and the online acceptance rule. Candidate-scene runs evaluate the promotion path under controlled conditions; long-term unattended deployment was not evaluated.