arXiv AI June 4, 2026 By Yitong Zhou, Yucong Luo, Mingyue Cheng, Qi Liu, Jiahao Wang, Daoyu Wang, Enhong Chen

Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs

arXiv:2506. 10630v3 Announce Type: replace-cross Abstract: To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techniques to data-driven deep learning architectures.

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 AI.

arXiv AI

Aug 25

Time Series Forecasting via Reasoning: A Slow-Thinking Approach with Reinforcement Fine-Tuned LLMs

arXiv:2506.10630v4 Announce Type: replace-cross Abstract: To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techni...

By Yitong Zhou, Yucong Luo, Mingyue Cheng, Qi Liu, Jiahao Wang, Daoyu Wang, Enhong Chen

llms reinforcement-learning fine-tuning safety

arXiv AI

Aug 21

TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning

arXiv:2510. 03519v3 Announce Type: replace-cross Abstract: Time series reasoning is crucial to decision-making in diverse domains, including finance, energy, and scientific discovery.

By Fangxu Yu, Hongyu Zhao, Tianyi Zhou

llms fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Aug 12

REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting

arXiv:2608. 10149v1 Announce Type: new Abstract: Due to the diversity of real-world time series, no single forecasting model consistently dominates across all samples.

By Xu Zhang, Chang Xu, Hui Sun, Nan Ma, Zijian Zhang, Peng Wang, Wei Wang, Li Zhao

llms fine-tuning benchmarks 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

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

Sep 15

TimeThink: Eliciting Compositional Reasoning in Timeseries Large Language Models

arXiv:2609.13457v1 Announce Type: new Abstract: Timeseries multimodal large language models (TS-MLLMs) have recently begun leveraging the reasoning capabilities of large language models (LLMs) for qu...

By Sudarshan Regmi, Arvind Pillai, Yu Yvonne Wu, Yuliang Chen, Bibek Panthi, Tess Z. Griffin, Michael V. Heinz, Lisa Marsch, Nicholas C. Jacobson, Andrew Campbell

llms reinforcement-learning multimodal benchmarks

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Related stories

Time Series Forecasting via Reasoning: A Slow-Thinking Approach with Reinforcement Fine-Tuned LLMs

TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning

REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting

Forecasting With LLMs: Improved Generalization Through Feature Steering

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

TimeThink: Eliciting Compositional Reasoning in Timeseries Large Language Models