Imagine predicting the stock market's twists and turns, or anticipating disease outbreaks before they happen. Predicting the future is a tantalizing prospect, and now, Large Language Models (LLMs) are stepping into the forecasting arena. Traditionally, LLMs excel at text analysis, but researchers are exploring their potential for crunching numerical data like time series—sequences of data points collected over time. One major hurdle is that LLMs typically handle one-dimensional data, while real-world forecasting often involves multiple variables (like temperature, humidity, and wind speed for weather prediction). Enter MultiCast, a novel approach that empowers LLMs to tackle multivariate time series forecasting without any prior training (zero-shot learning). MultiCast cleverly combines multiple data streams into a single input that LLMs can understand, using three innovative "multiplexing" techniques. It also uses a "quantization" trick to simplify the data, making it easier for LLMs to spot patterns while saving on computational costs. In tests on real-world datasets (gas emissions, electricity usage, and weather patterns), MultiCast held its own against established forecasting methods. While traditional methods sometimes outperformed MultiCast, the LLM-based approach shone in its ability to capture complex relationships between variables without extensive training. The research also revealed a fascinating trade-off: while analyzing each variable separately can be simpler, MultiCast's ability to consider multiple variables simultaneously offers a more holistic view, potentially leading to more accurate predictions. The future of MultiCast looks bright. As LLMs continue to evolve, MultiCast's performance is expected to improve, opening doors to more accurate and efficient forecasting across various fields. Imagine LLMs predicting equipment failures in factories, optimizing energy consumption in smart grids, or even personalizing medical treatments based on individual patient data. MultiCast is a step towards a future where LLMs can not only understand the present but also predict the future, with far-reaching implications for how we make decisions and interact with the world around us.