Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting. (arXiv:2202.08756v2 [cs.LG] UPDATED)

This paper presents a novel probabilistic forecasting method called ensemble
conformalized quantile regression (EnCQR). EnCQR constructs distribution-free
and approximately marginally valid prediction intervals (PIs), which are
suitable for nonstationary and heteroscedastic time series data. EnCQR can be
applied on top of a generic forecasting model, including deep learning
architectures. EnCQR exploits a bootstrap ensemble estimator, which enables the
use of conformal predictors for time series by removing the requirement of data
exchangeability. The ensemble learners are implemented as generic machine …

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