Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting
This addresses the need for reliable uncertainty quantification in time series forecasting, particularly for applications with varying data variability, though it is an incremental improvement combining existing techniques.
The paper tackles the problem of probabilistic time series forecasting by proposing ensemble conformalized quantile regression (EnCQR), which constructs distribution-free and approximately valid prediction intervals for nonstationary and heteroscedastic data, and it outperforms existing methods by providing sharper and more informative intervals.
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 learning algorithms performing quantile regression, which allow the length of the PIs to adapt to local variability in the data. In the experiments, we predict time series characterized by a different amount of heteroscedasticity. The results demonstrate that EnCQR outperforms models based only on quantile regression or conformal prediction, and it provides sharper, more informative, and valid PIs.