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

arXiv: https://arxiv.org/abs/2512.11251 · HTML: https://mlsanity.com/paper/2512.11251/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 12 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [One Fits All:Power General Time Series Analysis by Pretrained LM](https://mlsanity.com/paper/2302.11939/) (2023)
- [Monash Time Series Forecasting Archive](https://mlsanity.com/paper/2105.06643/) (2021)
- [PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting](https://mlsanity.com/paper/2210.08964/) (2022)
- [Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting](https://mlsanity.com/paper/2306.11025/) (2023)
- [FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis](https://mlsanity.com/paper/2308.01430/) (2023)

## Built on by (most cited first, 3 of 3)

- [Thoth: Mid-Training Bridges LLMs to Time Series Understanding](https://mlsanity.com/paper/2603.01042/) (2026)
- [Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Models](https://mlsanity.com/paper/2601.02968/) (2026)
- [WaveTLM: Reliable Time-Series Language Modeling through Task Compilation](https://mlsanity.com/paper/2609.18812/) (2026)

## Abstract (arXiv)

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-InsightsfootnoteAvailable at hrefhttps://huggingface.co/datasets/zhykoties/time-series-language-alignmenthttps://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 citepliu2023llava 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.