The code for the reproducibility of the experiments presented in the paper ResCP: Reservoir Conformal Prediction for Time Series Forecasting will become available upon publication.
Authors: Roberto Neglia, Andrea Cini, Michael M. Bronstein, Filippo Maria Bianchi.
Reservoir Conformal Prediction (ResCP) is a novel, training-free, scalable, localized conformal prediction (CP) method for time series where local similarity is gauged by relying on reservoir states. By sampling more residuals associated with similar states, we condition the estimates on specific dynamics of interest in the time series. This allows us to generate prediction intervals (PIs) that are more informative and adaptive to local patterns in the data, and we empirically demonstrate its effectiveness across various real-world datasets. We also prove that, under reasonable assumptions, ResCP achieves asymptotic conditional coverage.