  Predicting future values in multivariate time series is vital across various
domains. This work explores the use of large language models (LLMs) for this
task. However, LLMs typically handle one-dimensional data. We introduce
MultiCast, a zero-shot LLM-based approach for multivariate time series
forecasting. It allows LLMs to receive multivariate time series as input,
through three novel token multiplexing solutions that effectively reduce
dimensionality while preserving key repetitive patterns. Additionally, a
quantization scheme helps LLMs to better learn these patterns, while
significantly reducing token use for practical applications. We showcase the
performance of our approach in terms of RMSE and execution time against
state-of-the-art approaches on three real-world datasets.
