This paper provides an in-depth exploration of the application of Large Language Models (LLMs) in time series analysis, a domain that has traditionally relied on classical signal processing techniques and deep learning approaches. The authors address the challenge of bridging the gap between LLMs, which are trained on textual data, and the numerical nature of time series data. They propose five methodologies to bridge this gap: (1) direct prompting of LLMs, (2) time series quantization, (3) aligning techniques, (4) utilizing the vision modality as a bridging mechanism, and (5) integrating LLMs with tools. The paper also reviews existing multimodal datasets that incorporate both time series and text data, and discusses future research directions, including theoretical understanding, multimodal and multitask analysis, efficient algorithms, combining domain knowledge, and customization and privacy. The authors conclude by highlighting the potential of LLMs in time series analysis and the need for further research to fully realize their capabilities.This paper provides an in-depth exploration of the application of Large Language Models (LLMs) in time series analysis, a domain that has traditionally relied on classical signal processing techniques and deep learning approaches. The authors address the challenge of bridging the gap between LLMs, which are trained on textual data, and the numerical nature of time series data. They propose five methodologies to bridge this gap: (1) direct prompting of LLMs, (2) time series quantization, (3) aligning techniques, (4) utilizing the vision modality as a bridging mechanism, and (5) integrating LLMs with tools. The paper also reviews existing multimodal datasets that incorporate both time series and text data, and discusses future research directions, including theoretical understanding, multimodal and multitask analysis, efficient algorithms, combining domain knowledge, and customization and privacy. The authors conclude by highlighting the potential of LLMs in time series analysis and the need for further research to fully realize their capabilities.