Large Language Models (LLMs) have seen significant use in domains such as
natural language processing and computer vision. Going beyond text, image and
graphics, LLMs present a significant potential for analysis of time series
data, benefiting domains such as climate, IoT, healthcare, traffic, audio and
finance. This survey paper provides an in-depth exploration and a detailed
taxonomy of the various methodologies employed to harness the power of LLMs for
time series analysis. We address the inherent challenge of bridging the gap
between LLMs' original text data training and the numerical nature of time
series data, and explore strategies for transferring and distilling knowledge
from LLMs to numerical time series analysis. We detail various methodologies,
including (1) direct prompting of LLMs, (2) time series quantization, (3)
alignment techniques, (4) utilization of the vision modality as a bridging
mechanism, and (5) the combination of LLMs with tools. Additionally, this
survey offers a comprehensive overview of the existing multimodal time series
and text datasets and delves into the challenges and future opportunities of
this emerging field. We maintain an up-to-date Github repository which includes
all the papers and datasets discussed in the survey.

该综述论文详细探讨了利用大型语言模型进行时间序列分析的各种方法，包括直接提示、时间序列量化、对齐技术、利用视觉作为桥梁机制以及与其他工具的结合，同时提供了现有的多模态时间序列和文本数据集的综合概述，并分析了该新兴领域的挑战和未来机会。