Conformal prediction for dynamic time-series.
Chen Xu, Yao Xie · arXiv (Cornell University) · 2020
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.