arXiv Machine Learning June 4, 2026 By Zhongzheng Qiao, Sheng Pan, Anni Wang, Viktoriya Zhukova, Yong Liu, Xudong Jiang, Qingsong Wen, Mingsheng Long, Ming Jin, Chenghao Liu

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

Read the original on arXiv Machine Learning →

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.

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

Jul 3

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis

arXiv:2607. 01918v1 Announce Type: new Abstract: We present Zeus, a unified tuning-free Time Series Foundation Model (TSFM) that delivers superior performance across diverse analysis tasks without any task-specific fine-tuning.

By Yisong Fu, Zezhi Shao, Chengqing Yu, Yujie Li, Yongjun Xu, Xueqi Cheng, Fei Wang

llms nlp fine-tuning

arXiv AI

Sep 25

TimeBraid: Unifying Time Series and Language for Understanding and Forecasting

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.

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

llms benchmarks

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 AI

Jun 10

UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation

arXiv:2606. 10466v1 Announce Type: cross Abstract: In time-series generation, existing approaches typically handcraft ortrain a separate model for each dataset, which hinders their scalability and fails to leverage shared temporal structures across domains.

By Du Yin, Hao Xue, Jinliang Deng, Yang Yang, Shuang Ao, Arian Prabowo, Flora Salim

llms benchmarks

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 AI

Jun 16

TimeVista: Exploring and Exploiting Vision-Language Models as Judges for Time Series Forecasting

arXiv:2606. 16173v1 Announce Type: new Abstract: High-quality time series forecasting is pivotal for real-world decision-making.

By Zhi Chen, Yuxuan Wang, Jialong Wu, Yong Liu, Haoran Zhang, Xingjian Su, Jianmin Wang, Mingsheng Long

llms multimodal benchmarks

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Related stories

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis

TimeBraid: Unifying Time Series and Language for Understanding and Forecasting

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

UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation

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

TimeVista: Exploring and Exploiting Vision-Language Models as Judges for Time Series Forecasting