The survey explores the integration of Large Language Models (LLMs) in time series analytics, addressing the cross-modality gap between text and time series data. It categorizes existing methodologies, reviews key strategies for alignment and fusion, and evaluates their effectiveness through experiments on multimodal datasets. The study also outlines future research directions for enhancing LLM-based time series modeling.