Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting, which is a critical task across many domains, spanning healthcare, finance, climate, energy, and many more. In this paper, we propose a framework for rigorously evaluating the capabilities of LLMs on time series understanding, encompassing both univariate and multivariate forms. We introduce a comprehensive taxonomy of time series features, a critical framework that delineates various characteristics inherent in time series data. Leveraging this taxonomy, we have systematically designed and synthesized a diverse dataset of time series, embodying the different outlined features. This dataset acts as a solid foundation for assessing the proficiency of LLMs in comprehending time series. Our experiments shed light on the strengths and limitations of state-of-the-art LLMs in time series understanding, revealing which features these models readily comprehend effectively and where they falter. In addition, we uncover the sensitivity of LLMs to factors including the formatting of the data, the position of points queried within a series and the overall time series length.

提出了一个评估大型语言模型（LLM）在时间序列理解方面能力的框架，包括单变量和多变量形式；通过研究时间序列的特征以及设计和合成数据集来评估LLMs在时间序列理解方面的能力，并揭示了LLMs对数据格式、查询数据的位置和时间序列长度等因素的敏感性。

评估大型语言模型对时间序列特征理解的综合分类和基准