Conformal prediction interval for dynamic time-series
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We develop a method to build distribution-free prediction intervals in batches for time-series based on conformal inference, called |EnbPI| that wraps around any ensemble estimator to construct sequential prediction intervals. |EnbPI| is closely related to the conformal prediction (CP) framework but does not require data exchangeability. Theoretically, these intervals attain finite-sample, approximately valid average coverage for broad classes of regression functions and time-series with strongly mixing stochastic errors. Computationally, |EnbPI| requires no training of multiple ensemble estimators; it efficiently operates around an already trained ensemble estimator. In general, |EnbPI| is easy to implement, scalable to producing arbitrarily many prediction intervals sequentially, and well-suited to a wide range of regression functions. We perform extensive simulations and real-data analyses to demonstrate its effectiveness.
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