Insight Miner: A Time Series Analysis Dataset for Cross-Domain Alignment with Natural Language
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 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 TS-InsightsAvailable 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 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 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.
6940cc4e65f1e24a1177feaf
Yunkai Zhang
6940cc4e65f1e24a1177feb0
Yawen Zhang
6940cc4e65f1e24a1177feb1
Ming Zheng
6940cc4e65f1e24a1177feb2
Kezhen Chen
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Chongyang Gao
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Ruian Ge
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Siyuan Teng
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Amine Jelloul
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Jinmeng Rao
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Xiaoyuan Guo
6940cc4e65f1e24a1177feb9
Chiang-Wei Fang
6940cc4e65f1e24a1177feba
Zeyu Zheng
6940cc4e65f1e24a1177febb
Jie Yang
Insight Miner: A Time Series Analysis Dataset for Cross-Domain Alignment with Natural Language
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 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 TS-InsightsAvailable 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 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 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.
A large-scale multimodal model called Insight Miner is proposed for generating comprehensive time-series descriptions enriched with domain-specific knowledge, utilizing a novel agentic workflow and a newly constructed dataset for time series and language alignment.
multimodal model time-series data domain-specific knowledge agentic workflow time-series language alignment instruction tuning