  We conduct an extensive empirical study on short-term electricity price
forecasting (EPF) to address the long-standing question if the optimal model
structure for EPF is univariate or multivariate. We provide evidence that
despite a minor edge in predictive performance overall, the multivariate
modeling framework does not uniformly outperform the univariate one across all
12 considered datasets, seasons of the year or hours of the day, and at times
is outperformed by the latter. This is an indication that combining advanced
structures or the corresponding forecasts from both modeling approaches can
bring a further improvement in forecasting accuracy. We show that this indeed
can be the case, even for a simple averaging scheme involving only two models.
Finally, we also analyze variable selection for the best performing
high-dimensional lasso-type models, thus provide guidelines to structuring
better performing forecasting model designs.
