CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting
arXiv:2604.18305v1 Announce Type: new
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 a…