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.
@article{arxiv.2512.11251,
title = {Insight Miner: A Time Series Analysis Dataset for Cross-Domain Alignment with Natural Language},
author = {Yunkai Zhang and Yawen Zhang and Ming Zheng and Kezhen Chen and Chongyang Gao and Ruian Ge and Siyuan Teng and Amine Jelloul and Jinmeng Rao and Xiaoyuan Guo and Chiang-Wei Fang and Zeyu Zheng and Jie Yang},
journal= {arXiv preprint arXiv:2512.11251},
year = {2025}
}