Agentic Time Series Forecasting

Latest papers

14 papers2 papers in the last four weeks, against 2 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
Rethinking Agentic Model Orchestration for Time Series Forecasting
Oct 21, 2026Tong Guan, Tianxiang Zhan, Shirui Pan +1Agentic Time Series Forecasting Time Series Forecasting
Griffith University · Zhejiang University
TimeRSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models
Oct 14, 2026Haoxin Liu, Deqing Fu, Lucheng Fu +4Agentic Time Series Forecasting Time Series Forecasting
Georgia Institute of Technology · University of Southern California · Samay AI
Forecast Workflow Bench: Evaluating Language-Model Decisions with Budgeted Forecast Tools
Sep 23, 2026Shunya NagashimaForecasting Benchmarks LLM Tool Use
Neurogica Inc.
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
LLM Agents for Time-Series: A Survey
Aug 26, 2026Yilong Chen, Xiao Qin, Chenghao Liu +3LLM-Based Time Series Anomaly Detection LLM Agents
Northwestern University · Datadog AI Research · Nokia
TimeSage-EV: A Live Benchmark for Agentic Time Series Analysis in Evolving Environments
Aug 14, 2026Qingren Yao, Yaxuan Kong, Yuqi Nie +6Agentic Time Series Forecasting Time Series QA
Eindhoven University of Technology · University of Oxford · VulpiVox Intelligence +2
CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting
Aug 4, 2026Xiaoyu Tao, Mingyue Cheng, Bokai Pan +6Time Series Forecasting LLM-Based Time Series Forecasting
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui Province, China
Harnessing Generalist Agents for Contextualized Time Series
Jun 3, 2026Zihao Li, Kaifeng Jin, Yuanchen Bei +8Time Series Reasoning LLM Agent Orchestration
University of Illinois Urbana-Champaign
Bridging the Last Mile of Time Series Forecasting with LLM Agents
Jun 1, 2026Yuhua Liao, Zetian Wang, Qiangqiang Nie +1Time Series Forecasting LLM-Based Time Series Forecasting
Trip.com Group, Shanghai, China
KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning
May 28, 2026Kun Feng, Ziwei Shan, Yuchen Fang +6Multivariate Time Series Forecasting Zero-Shot Time Series Forecasting
ShanghaiTech University · Ant Group
Dr-CiK: A Testbed for Foresight-Driven Agents
May 27, 2026Yihong Tang, Andrew Robert Williams, Arjun Ashok +6Time Series Forecasting Forecasting Benchmarks
McGill University · ServiceNow Research · Université de Montréal +3
CastFlow: Bridging Evidence Acquisition and Accurate Prediction in Agentic Time Series Forecasting
Apr 30, 2026Bokai Pan, Mingyue Cheng, Xiaoyu Tao +6Time Series Forecasting LLM-Based Time Series Forecasting
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei 230026, China
Learning Agentic Time Series Forecasting via Trajectory Distillation and Memory-Based Reinforcement Learning
Feb 14, 2026Xiaoyu Tao, Mingyue Cheng, Yaguo Liu +4Agentic Time Series Forecasting Time Series Forecasting
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui Province, China

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.