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 propose a novel modeling approach named ContextAware ARLLM CAARL that provides an interpretable framework to decode the contextual dynamics influencing changes in coevolving series CAARL decomposes time series into autoregressive segments constructs a temporal dependency graph and serializes this graph into a narrative to allow processing by LLM This design yields a chainofthoughtlike reasoning path where intermediate steps capture contextual dynamics and guide forecasts in a transparent manner By linking prediction to explicit reasoning traces CAARL enhances interpretability while maintaining accuracy Experiments on realworld datasets validate its effectiveness positioning CAARL as a competitive and interpretable alternative to stateoftheart forecasting methods
@article{arxiv.2604.18305,
title = {CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting},
author = {Etienne Tajeuna and Patrick Asante Owusu and Armelle Brun and Shengrui Wang},
journal= {arXiv preprint arXiv:2604.18305},
year = {2026}
}