Smart baseline forecasting models fed with easy-to-obtain data.
Significant improvements over the naive persistence models.
Fixed models, trained with samples from all periods of the year.
Moving models, constantly updated based on the features of the test sample.
Machine learning algorithms and automation of the calibration process using GAs.