Hugging Face Trending Papers July 27, 2026

LLM as Forecasting Planner: Training-Free Text Conditioning for Time-Series Foundation Models

Read the original on Hugging Face Trending Papers →

Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account for events and constraints that the past alone cannot reveal. This requires both reliable numerical forecasting and the ability to interpret contextual information.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

llms

arXiv AI

Jul 29

LLM as Forecasting Planner: Training-Free Text Conditioning for Time-Series Foundation Models

arXiv:2607. 24892v1 Announce Type: cross Abstract: Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account for events and constraints that the past alone cannot reveal.

By Huu Hiep Nguyen, Dung Nguyen, Minh Hoang Nguyen, Dai Do, Hung Le

llms

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 10

MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning

arXiv:2602. 03164v2 Announce Type: replace-cross Abstract: Time series forecasting (TSF) plays a critical role in decision-making for many real-world applications.

By Xiaoyu Tao, Mingyue Cheng, Ze Guo, Shuo Yu, Yaguo Liu, Qi Liu, Shijin Wang

llms

arXiv AI

Jul 14

Beyond Na\"ive Prompting: Strategies for Improved Context-aided Forecasting with LLMs

arXiv:2508. 09904v3 Announce Type: replace-cross Abstract: Real-world forecasting requires models to integrate not only historical data but also relevant contextual information provided in textual form.

By Arjun Ashok, Andrew Robert Williams, Vincent Zhihao Zheng, Irina Rish, Nicolas Chapados, \'Etienne Marcotte, Valentina Zantedeschi, Alexandre Drouin

llms

arXiv Machine Learning

Aug 4

ReasonCast: Towards Explainable Time Series Forecasting with Reasoning

arXiv:2608. 01875v1 Announce Type: cross Abstract: Most time series (TS) models are specialized for a single task, either understanding (i.

By Seunghan Lee, Jun Seo, Jaehoon Lee, Junhyeok Kang, Sangjun Han, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Soonyoung Lee, Wonbin Ahn

llms fine-tuning benchmarks

arXiv AI

Aug 3

TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning

arXiv:2605. 00015v2 Announce Type: replace-cross Abstract: Time Series Foundation Models (TSFMs) have demonstrated strong generalization capability and data efficiency in time series forecasting through large-scale pretraining.

By Siyang Li, Yize Chen, Zijie Zhu, Yuxin Pan, Yan Guo, Ming Huang, Hui Xiong

reinforcement-learning fine-tuning benchmarks

Related stories

LLM as Forecasting Planner: Training-Free Text Conditioning for Time-Series Foundation Models

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

MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning

Beyond Na\"ive Prompting: Strategies for Improved Context-aided Forecasting with LLMs

ReasonCast: Towards Explainable Time Series Forecasting with Reasoning

TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning