IAI

The paper “Probabilistic energy forecasting using the nearest neighbors quantile filter and quantile regression” by Jorge Ángel González Ordiano, Lutz Gröll, Ralf Mikut and Veit Hagenmeyer received the “2019-2020 Outstanding Paper Award” of the International Journal of Forecasting.

The authors developed a nonparametric approach to probabilistic forecasting, which they refer to as nearest neighbors quantile filter (NNQF), with an application to solar energy forecasting (GEFCom 2014 dataset) to illustrate its workings and performance. The main argument is that the approach is very easy to use, very cheap computationally, while being very close in terms of performance to the best entries of the GEFcom 2014 competition.