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• W3207173744 abstract "We develop a general framework constructing distribution-free prediction intervals for dynamic time series. We show that our intervals asymptotically attain valid conditional and marginal coverages for a broad class of predictions functions and time series. We also show that our interval width converges to that of the oracle prediction interval asymptotically. Moreover, we introduce a computationally efficient algorithm called verb|EnbPI| that wraps around ensemble predictors, which is closely related to conformal prediction (CP) but does not require data exchangeability. verb|EnbPI| avoids data-splitting and is computationally efficient by avoiding retraining and thus scalable to sequentially producing prediction intervals. We perform extensive simulation and real-data analyses to demonstrate its effectiveness compared with existing methods." @default.
• W3207173744 created "2021-10-25" @default.
• W3207173744 creator A5023656131 @default.
• W3207173744 creator A5047736740 @default.
• W3207173744 date "2020-10-18" @default.
• W3207173744 modified "2023-09-27" @default.
• W3207173744 title "Conformal prediction for dynamic time-series." @default.
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• W3207173744 hasPublicationYear "2020" @default.
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