Insight Miner: A Time Series Analysis Dataset for Cross-Domain Alignment with Natural Language

Yunkai Zhang; Yawen Zhang; Ming Zheng; Kezhen Chen; Chongyang Gao; Ruian Ge; Siyuan Teng; Amine Jelloul; Jinmeng Rao; Xiaoyuan Guo; Chiang-Wei Fang; Zeyu Zheng; Jie Yang

arXiv · 2512.11251

Insight Miner: A Time Series Analysis Dataset for Cross-Domain Alignment with Natural Language

Yunkai Zhang·Yawen Zhang·Ming Zheng·Kezhen Chen·Chongyang Gao·Ruian Ge·Siyuan Teng·Amine Jelloul·Jinmeng Rao·Xiaoyuan Guo·Chiang-Wei Fang·Zeyu Zheng·Jie Yang

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Abstract

Time-series data is critical across many scientific and industrial domains, including environmental analysis, agriculture, transportation, and finance. However, mining insights from this data typically requires deep domain expertise, a process that is both time-consuming and labor-intensive. In this paper, we propose \textbf{Insight Miner}, a large-scale multimodal model (LMM) designed to generate high-quality, comprehensive time-series descriptions enriched with domain-specific knowledge. To facilitate this, we introduce \textbf{TS-Insights}\footnote{Available at \href{https://huggingface.co/datasets/zhykoties/time-series-language-alignment}{https://huggingface.co/datasets/zhykoties/time-series-language-alignment}.}, the first general-domain dataset for time series and language alignment. TS-Insights contains 100k time-series windows sampled from 20 forecasting datasets. We construct this dataset using a novel \textbf{agentic workflow}, where we use statistical tools to extract features from raw time series before synthesizing them into coherent trend descriptions with GPT-4. Following instruction tuning on TS-Insights, Insight Miner outperforms state-of-the-art multimodal models, such as LLaVA \citep{liu2023llava} and GPT-4, in generating time-series descriptions and insights. Our findings suggest a promising direction for leveraging LMMs in time series analysis, and serve as a foundational step toward enabling LLMs to interpret time series as a native input modality.

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Yunkai Zhang, Yawen Zhang, Ming Zheng, Kezhen Chen, Chongyang Gao, Ruian Ge, Siyuan Teng, Amine Jelloul, Jinmeng Rao, Xiaoyuan Guo, Chiang-Wei Fang, Zeyu Zheng, Jie Yang. 2025-12-12. Insight Miner: A Time Series Analysis Dataset for Cross-Domain Alignment with Natural Language. https://arxiv.org/abs/2512.11251

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