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Estimating quantiles of an outcome conditional on covariates is offundamental interest in statistics with broad application in probabilisticprediction and forecasting. We propose an ensemble method for conditionalquantile estimation, Quantile Super Learning, that combines predictions frommultiple candidate algorithms based on their empirical performance measuredwith respect to a cross-validated empirical risk of the quantile loss function.We present theoretical guarantees for both iid and online data scenarios. Theperformance of our approach for quantile estimation and in forming predictionintervals is tested in simulation studies. Two case studies related to solarenergy are used to illustrate Quantile Super Learning: in an iid setting, wepredict the physical properties of perovskite materials for photovoltaic cells,and in an online setting we forecast ground solar irradiance based on outputfrom dynamic weather ensemble models.

Quantile Super Learning for independent and online settings with application to solar power forecasting