Insight Miner: A Time Series Analysis Dataset for Cross-Domain Alignment with Natural Language
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917141388722176 |
|---|---|
| author | Zhang, Yunkai Zhang, Yawen Zheng, Ming Chen, Kezhen Gao, Chongyang Ge, Ruian Teng, Siyuan Jelloul, Amine Rao, Jinmeng Guo, Xiaoyuan Fang, Chiang-Wei Zheng, Zeyu Yang, Jie |
| author_facet | Zhang, Yunkai Zhang, Yawen Zheng, Ming Chen, Kezhen Gao, Chongyang Ge, Ruian Teng, Siyuan Jelloul, Amine Rao, Jinmeng Guo, Xiaoyuan Fang, Chiang-Wei Zheng, Zeyu Yang, Jie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11251 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Insight Miner: A Time Series Analysis Dataset for Cross-Domain Alignment with Natural Language Zhang, Yunkai Zhang, Yawen Zheng, Ming Chen, Kezhen Gao, Chongyang Ge, Ruian Teng, Siyuan Jelloul, Amine Rao, Jinmeng Guo, Xiaoyuan Fang, Chiang-Wei Zheng, Zeyu Yang, Jie Machine Learning 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. |
| title | Insight Miner: A Time Series Analysis Dataset for Cross-Domain Alignment with Natural Language |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.11251 |