The field of time series forecasting is experiencing a significant shift with the integration of Large Language Models (LLMs). Recent developments have focused on leveraging LLMs to improve forecasting accuracy and provide more informative predictions. One notable direction is the use of LLMs as semantic guidance modules, which refine traditional predictions rather than replacing them. This approach has shown promise in multivariate time series forecasting, where LLMs can provide explicit semantic guidance to improve forecasting performance. Another area of research is the use of LLMs to analyze textual data, such as news and social media posts, to inform time series forecasting. However, concerns about intellectual property protection and the potential misuse of LLMs have also been raised, leading to the development of watermarking techniques to protect generated time series data. Noteworthy papers include: DualSG, which proposes a dual-stream framework that provides explicit semantic guidance for multivariate time series forecasting. Waltz, which introduces a novel post-hoc watermarking framework for Large Language Model-based Time Series Forecasting models.