arXiv AI

TSQueryBench: LLM-as-a-Judge for Time Series Explanations

arXiv:2604. 02118v2 Announce Type: replace Abstract: Natural language explanations of time series data are increasingly produced by foundation models in high stakes domains, making factual correctness critical.

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 AI

Jun 2

TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning

arXiv:2606. 01498v1 Announce Type: cross Abstract: Time series data inform critical decisions across many real-world domains.

By Yaxuan Kong, Qingren Yao, Yuqi Nie, Yichen Li, Yilei Shao, Stefan Zohren, Anna Vettoruzzo, Joaquin Vanschoren, Ming Jin, Qingsong Wen

llms agents benchmarks

arXiv AI

Jun 8

TSAQA: Time Series Analysis Question And Answering Benchmark

arXiv:2601. 23204v2 Announce Type: replace Abstract: Time series data are integral to critical applications across domains such as finance, healthcare, transportation, and environmental science.

By Baoyu Jing, Sanhorn Chen, Lecheng Zheng, Boyu Liu, Zihao Li, Jiaru Zou, Tianxin Wei, Zhining Liu, Zhichen Zeng, Ruizhong Qiu, Xiao Lin, Yuchen Yan, Dongqi Fu, Jingchao Ni, Jingrui He, Hanghang Tong

llms nlp fine-tuning benchmarks

arXiv AI

Jun 3

TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning

arXiv:2606. 03629v1 Announce Type: new Abstract: Assessing the quality of time series (TS) data is fundamental yet inherently challenging due to the multifaceted nature of quality dimensions.

By Shunyu Wu, Dan Li, Haozheng Ye, Weibin Feng, Jian Lou, Bo Zhang, Wenjie Feng, Chenjuan Guo, See-Kiong Ng

llms agents benchmarks data-engineering

arXiv AI

Jul 15

Scaling Point-in-Time Language Models

arXiv:2607. 11889v1 Announce Type: cross Abstract: Large language models trained on unrestricted internet corpora inevitably embed information from the future, introducing lookahead bias that compromises the validity of backtests and causal inference in finance and the social sciences.

By Bryan Kelly, Semyon Malamud, Johannes Schwab, Teng Andrea Xu

llms fine-tuning benchmarks safety

arXiv AI

Jul 22

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series

arXiv:2607. 18271v1 Announce Type: new Abstract: Time series forecasts are widely used in decision-critical domains, where they are rarely consumed without accompanying explanations.

By Ria Mundhra, Gustavo Sato dos Santos, Michael Benedikt

llms fine-tuning safety

arXiv AI

Jun 16

Towards Verifiable Agentic Data Science: Solving Irregular TSQA Via Tool-Grounded Reasoning

arXiv:2606. 15107v1 Announce Type: new Abstract: Time series data in real-world deployments is overwhelmingly irregular.

By Sanhorn Chen, Xiaoyang Chen, Boyu Liu, Roy Zhao

llms agents nlp benchmarks

arXiv AI

Jun 11

Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases

arXiv:2602. 17001v3 Announce Type: replace Abstract: Natural Language Querying for Time Series Databases (NLQ4TSDB) aims to assist non-expert users retrieve meaningful events, intervals, and summaries from massive temporal records.

By Zhao Tan, Yiji Zhao, Shiyu Wang, Chang Xu, Yuxuan Liang, Xiping Liu, Shirui Pan, Ming Jin

benchmarks data-engineering

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 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 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

arXiv AI

Jul 1

SAGE: A Search-AuGmented Evaluation of Large Language Models on Free-Form QA

arXiv:2504. 07385v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) become increasingly used for question-answering (QA), relying on static, pre-annotated references for evaluation poses significant challenges in cost, scalability, and completeness.

By Sher Badshah, Ali Emami, Hassan Sajjad

llms agents benchmarks

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