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

TL;DR

Insight Miner introduces a large-scale dataset and a multimodal model that generate detailed, domain-enriched descriptions of time series data, advancing automatic insight extraction across various scientific fields.

Contribution

The paper presents TS-Insights, the first dataset for time series and language alignment, and a multimodal model trained on it, outperforming existing models in time series description tasks.

Findings

Insight Miner surpasses state-of-the-art models in time series description.

TS-Insights enables effective training of multimodal models for time series analysis.

The approach demonstrates potential for LLMs to interpret time series as a native modality.

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.