Competitive Online Regression under Continuous Ranked Probability Score


Raisa Dzhamtyrova, Yuri Kalnishkan ;
Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR 105:178-195, 2019.


We consider the framework of competitive prediction when one provides guarantees compared to other predictive models that are called experts. We propose the algorithm that combines point predictions of an infinite pool of linear experts and outputs probability forecasts in the form of cumulative distribution functions. We evaluate the quality of probabilistic prediction by the continuous ranked probability score (CRPS), which is a widely used proper scoring rule. We provide a strategy that allows us to “track the best expert” and derive the theoretical bound on the discounted loss of the strategy. Experimental results on synthetic data and solar power data show that the theoretical bounds of our algorithm are not violated. Also the algorithm performs close to and sometimes outperforms the retrospectively best quantile regression.

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