MultiCast: Zero-Shot Multivariate Time Series Forecasting Using LLMs
By: Georgios Chatzigeorgakidis, Konstantinos Lentzos, Dimitrios Skoutas Posted: May 24, 2024
arXiv:2405.14748v1 Announce Type: cross
Abstract: 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.
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