Thick as THieFs: Temporal coherent forecast combination for day-ahead electricity prices
TL;DR
Using the base forecasts of four model classes for the German and Spanish day-ahead markets, the combined forecasts significantly outperform both the base forecasts and the best reconciled expert at nearly every temporal level.
Abstract
Day-ahead electricity prices are forecast by many competing models and at several temporal granularities, from hourly prices to block and baseload products. The resulting forecasts suffer from two distinct problems: forecasts produced at different granularities are incoherent, as the aggregates do not match the averages of their components, and no single model is the most accurate in every market, period and level. We develop a temporal coherent combination approach that addresses both problems in one step, pooling the forecasts that the competing experts produce at all the levels of a temporal hierarchy into a single forecast that satisfies the aggregation constraints and has minimum error variance among the linear unbiased combinations. Since the optimal solution depends on a high-dimensional error covariance matrix, we compare different estimators obtained by crossing correlation structures with linear and nonlinear shrinkage approaches. Using the base forecasts of four model classes for the German and Spanish day-ahead markets, the combined forecasts significantly outperform both the base forecasts and the best reconciled expert at nearly every temporal level.