arXiv AI September 25, 2026 By Xinyue Wang, Jiacheng Pang, Kun Zhou, Kexin Zhang, Defu Cao, Fan Feng, Faisal, Songyao Jin, Yan Liu, Biwei Huang

TimeBraid: Unifying Time Series and Language for Understanding and Forecasting

Read the original on arXiv AI →

TimeBraid is a family of unified models that combine pretrained language models with pretrained time‑series foundation models using interleaved global residual attention layers. The models inherit instruction following, reasoning, and continuous‑signal perception, fusing both modalities into a shared representation space for understanding and generation. The design focuses on aligning representation spaces, grounding language in temporal structure, balancing understanding with generation, and maintaining stable joint optimization, supported by 2.2 M curated series‑text pairs and 4.9 M instruction‑tuning samples. Across diverse benchmarks, TimeBraid competes with larger general‑purpose and task‑specific models.

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

Jun 9

InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs

arXiv:2606. 08601v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting.

By Peiliang Gong, Emadeldeen Eldele, Chenyu Liu, Ziyu Jia, Yi Ding, Xinliang Zhou, Lianchao Gu, Qi Zhu, Yang Liu, Daoqiang Zhang, Xiaoli Li

llms benchmarks safety

arXiv Machine Learning

3d ago

A Foundation Model for Instruction-Conditioned In-Context Time Series Tasks

arXiv:2603.22586v4 Announce Type: replace Abstract: In-context learning (ICL) enables task adaptation at inference time by conditioning on demonstrations rather than updating model parameters. Althou...

By Anish Saha, Konstantin Shmakov

llms benchmarks

arXiv AI

Aug 25

NeST: Neighborhood-aware semantic alignment and temporal modulation for LLM based time series forecasting

NeST is a framework that adapts large language models (LLMs) for continuous time‑series forecasting by creating neighborhood‑aware text prototypes and aligning them with temporal representations through a nearest‑neighbor contrastive objective. It retrieves the most relevant prototypes and uses them to conditionally modulate time‑series features, enabling more effective integration of textual and temporal information. Experiments show that NeST outperforms state‑of‑the‑art methods on eight benchmarks, reduces MSE by 1.2% for long‑term forecasting, improves zero‑shot forecasting by 4.9%, and boosts R² by 3.3% on a real‑world photovoltaic power forecasting task.

By Jayanie Bogahawatte, Sachith Seneviratne, Maneesha Perera, Saman Halgamuge

llms rag fine-tuning benchmarks safety

arXiv AI

Jun 12

Representing Time Series as Structured Programs for LLM Reasoning

arXiv:2606. 12481v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong reasoning and instruction-following capabilities, making them potentially powerful tools for time-series analysis.

By Jaeho Kim, Changhun Oh, Seokhyun Lee, Irina Rish, Changhee Lee

llms nlp

arXiv Machine Learning

Jun 4

It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks

arXiv:2602. 12147v4 Announce Type: replace Abstract: Time series foundation models (TSFMs) are revolutionizing the forecasting landscape from specific dataset modeling to generalizable task evaluation.

By Zhongzheng Qiao, Sheng Pan, Anni Wang, Viktoriya Zhukova, Yong Liu, Xudong Jiang, Qingsong Wen, Mingsheng Long, Ming Jin, Chenghao Liu

llms benchmarks

arXiv AI

Jun 4

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.

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

llms reinforcement-learning fine-tuning safety

One email a morning, machine-written

Related stories

InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs

A Foundation Model for Instruction-Conditioned In-Context Time Series Tasks

NeST: Neighborhood-aware semantic alignment and temporal modulation for LLM based time series forecasting

Representing Time Series as Structured Programs for LLM Reasoning

It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks

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