CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting

Etienne Tajeuna, Patrick Asante Owusu, Armelle Brun +1

In this paper we investigate forecasting coevolving time series that feature intricate dependencies and nonstationary dynamics by using an LLM Large Language Models approach We pro…