The new paper “Relational Conformal Prediction for Correlated Time Series” by Andrea Cini and co-authors is out!

We propose a novel conformal prediction method based on graph deep learning. Our method can be applied on top of any time series predictor, can learn the relationships across the time series and, thanks to an adaptive component, can handle non-exchangeable data and nonstationarity in the time series.

The preprint is available on Arxiv.