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 has been a popular method for uncertainty quantification because it is distribution-free%2C model-agnostic%2C and theoretically sound. For forecasting problems in supervised learning%2C most CP methods focus on building prediction intervals for univariate responses. In this work%2C we develop a sequential CP method called %24texttt{MultiDimSPCI}%24 that builds prediction %24textit{regions}%24 for a multivariate response%2C especially in the context of multivariate time series%2C which are not exchangeable. Theoretically%2C we estimate %24textit{finite-sample}%24 high-probability bounds on the conditional coverage gap. Empirically%2C we demonstrate that %24texttt{MultiDimSPCI}%24 maintains valid coverage on a wide range of multivariate time series while producing smaller prediction regions than CP and non-CP baselines.?quality=80&w=800)
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