  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)
aligning 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.
