The recent advancements in the integration of Large Language Models (LLMs) with time series analysis have significantly enhanced the field's capabilities. Researchers are increasingly focusing on leveraging LLMs not just for pattern recognition but also for deeper semantic understanding and integration with traditional time series models. This trend is evident in the development of frameworks that combine LLM insights with traditional time series modeling techniques, such as mutual information maximization and sample reweighting, to improve predictive accuracy across various tasks. Additionally, there is a growing emphasis on multi-agent systems for automated annotation and classification of time series data, which addresses the challenge of obtaining high-quality annotations in critical domains. The integration of hierarchical and multimodal approaches, along with semantic space alignment, is also advancing the classification of multivariate time series data. These innovations are pushing the boundaries of what LLMs can achieve in time series analysis, from anomaly detection to long-term forecasting, and are setting new benchmarks in performance.