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

Etienne Tajeuna; Patrick Asante Owusu; Armelle Brun; Shengrui Wang

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arXiv · 2604.18305

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

Etienne Tajeuna·Patrick Asante Owusu·Armelle Brun·Shengrui Wang

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Abstract

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

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Etienne Tajeuna, Patrick Asante Owusu, Armelle Brun, Shengrui Wang. 2026-04-20. CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting. https://arxiv.org/abs/2604.18305

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