Computing electricity spot price prediction intervals using quantile regression and forecast averaging
We examine possible accuracy gains from forecast averaging in the context of interval forecasts of electricity spot prices. First, we test whether constructing empirical prediction intervals (PI) from combined electricity spot price forecasts leads to better forecasts than those obtained from individual methods. Next, we propose a new method for constructing PIQuantile Regression Averaging (QRA)which utilizes the concept of quantile regression and a pool of point forecasts of individual (i.e. not combined) models. While the empirical PI from combined forecasts do not provide significant gains, the QRA-based PI are found to be more accurate than those of the best individual modelthe smoothed nonparametric autoregressive model.
electricity spot price prediction interval quantile regression quantile regression averaging forecasts combination