Conformal prediction for multi-dimensional time series by ellipsoidal sets

arXiv:2403.03850v1 Announce Type: cross
Abstract: Conformal prediction (CP) has been a popular method for uncertainty quantification because it is distribution-free, model-agnostic, and theoretically sound. For forecasting problems in supervised learning, most CP methods focus on building prediction intervals for univariate responses. In this work, we develop a sequential CP method called $\texttt{MultiDimSPCI}$ that builds prediction regions for a multivariate response, especially in the context of multivariate time series, which are not exchangeable. Theoretically, we estimate finite-sample high-probability bounds on the …

abstract arxiv building cs.lg distribution focus forecasting free model-agnostic popular prediction quantification responses series sound stat.ml supervised learning time series type uncertainty work