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。