arXiv Machine Learning September 22, 2026 By Minkyoung Kim, Daeun Ji, Yohan Lee, Beomsoo Kim, Beakcheol Jang

CTRL: Control-Based Time Series Forecasting with LLM-Guided Residual Learning

Read the original on arXiv Machine Learning →

CTRL is a new framework for time‑series forecasting that separates semantic reasoning from quantitative prediction. It uses a frozen backbone to produce base forecasts, while LLM agents act as controllers that analyze prediction errors by decomposing them into trend, seasonal, and irregular components. The agents generate compact control signals that a lightweight residual decoder uses to correct the forecasts, and the system can adapt at test time to distribution shifts with only a few LLM calls.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

2d ago

OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoning

arXiv:2609.40265v1 Announce Type: new Abstract: Real-world time-series applications increasingly require models that can handle time series forecasting, context-conditioned prediction, and language-b...

By Tony Chen, Timo Stoffregen, Maxwell Xu, Thomas Kaar, Martin Maritsch, Geremia Pompei, Nicolas Zumarraga, Robert Jakob, Paul Schmiedmayer, Patrick Langer, Juncheng Liu

llms fine-tuning benchmarks

arXiv AI

Aug 25

LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications

arXiv:2608.23058v1 Announce Type: new Abstract: Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external too...

By Xiaogang Xu, Jiaqi Tang, Jianmin Chen, Yingying Yan, Zhenchao Tang, Xiangxin Zhou, Xiaobin Hu, Wei Wei, Jinfeng Wu, Qifeng Chen, Lu Zhou, Jiafei Wu, Zhe Liu, Jianwei Yin, Weimin Zheng

llms rag agents benchmarks

arXiv Machine Learning

Aug 18

Closing the Loop: A Control-Theoretic Framework for Provably Stable Time Series Forecasting with LLMs

arXiv:2602. 12756v2 Announce Type: replace Abstract: Large Language Models (LLMs) have recently shown exceptional potential in time series forecasting (TSF), leveraging their inherent sequential reasoning capabilities to model complex temporal dynamics.

By Xingyu Zhang, Jingyao Wang, Zeen Song, Changwen Zheng, Wenwen Qiang

llms benchmarks

arXiv AI

Aug 5

CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting

arXiv:2608. 03031v1 Announce Type: new Abstract: Time series forecasting is fundamental to decision-making in complex systems, where future dynamics are influenced not only by historical observations but also by evolving contextual features.

By Xiaoyu Tao, Mingyue Cheng, Bokai Pan, Chuang Jiang, Huanjian Zhang, Tian Gao, Yaguo Liu, Qi Liu, Enhong Chen

llms agents reinforcement-learning fine-tuning

arXiv AI

Jun 2

Bridging the Last Mile of Time Series Forecasting with LLM Agents

arXiv:2606. 02497v1 Announce Type: new Abstract: Time series forecasting has advanced rapidly, especially with the emergence of foundation models that show strong zero-shot performance on numerical extrapolation.

By Yuhua Liao, Zetian Wang, Qiangqiang Nie, Zhenhua Zhang

llms agents safety

arXiv Machine Learning

Jun 26

Forecasting With LLMs: Improved Generalization Through Feature Steering

arXiv:2606. 27199v1 Announce Type: cross Abstract: Successful forecasting involves identifying patterns between historical and future states of the world which generalize to future observations.

By Humzah Merchant, Bradford Levy

llms safety

One email a morning, machine-written

Related stories

OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoning

LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications

Closing the Loop: A Control-Theoretic Framework for Provably Stable Time Series Forecasting with LLMs

CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting

Bridging the Last Mile of Time Series Forecasting with LLM Agents

Forecasting With LLMs: Improved Generalization Through Feature Steering