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In this session, we explore the latest developments in using Large Language Models (LLMs) for time series forecasting. We cover techniques ranging from general-use LLMs like GPT-4 and Llama 3, using methods like PromptCast or LLMTime, to specialized foundation models designed specifically for time series forecasting, like Amazon's Chronos and Google's TimesFM. We discuss the performance of these models through benchmarks conducted by members of the forecasting community on open datasets. This presentation is designed for a general audience without specific forecasting experience, providing an update on the ongoing work in this area and highlighting contributions from various teams. Join us to gain insights into current trends and advancements in the field of time series forecasting.