TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning
The paper introduces TSQAgent, a framework designed to improve the assessment of time series data quality using large language models. It highlights the challenges faced by existing models in identifying relevant quality dimensions and performing grounded comparisons. The proposed framework aims to enhance the capabilities of these models through a collaborative approach involving focused dimension selection and quantitative analysis.
- ▪Assessing time series data quality is challenging due to its multifaceted nature.
- ▪Current large language models struggle with identifying relevant quality dimensions and performing evidence-grounded comparisons.
- ▪TSQAgent introduces a collaborative framework with roles for focused dimension selection, quantitative analysis, and final judgment aggregation.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2606.03629 |
| Publication time | Wed, 03 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-03T04:11:55.408Z |
| Last seen | 2026-06-03T04:11:55.408Z |
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| Summary source text | contentText |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2606.03629 (cs) [Submitted on 2 Jun 2026] Title:TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning Authors:Shunyu Wu, Dan Li, Haozheng Ye, Weibin Feng, Jian Lou, Bo Zhang, Wenjie Feng, Chenjuan Guo, See-Kiong Ng View a PDF of the paper titled TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning, by Shunyu Wu and 8 other authors View PDF HTML (experimental) Abstract:Assessing the quality of time series (TS) data is fundamental yet inherently challenging due to the multifaceted nature of quality dimensions. Recently, large language models (LLMs) have emerged as a promising paradigm for TS quality assessment via pairwise comparison and per-dimension evaluation.
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