Temperature prediction with expert agregation

A lot of Numerical Weather Prediction (NWP) models and their associated Model Output Statistics (MOS) are available. Expert aggregation has a bunch of advantages to deal with all these models, like being online, adaptive to model changes and having theoretical guarantees. With a new expert aggregation algorithm - FSBOA - a combination of BOA (Wintenberger 2017) and FS (Herbster and Warmuth 1998), and the use of a sliding window, we improved the temperature prediction on average without loosing too much reactivity of the expert weights. We also tested several aggregation strategies in order to improve the prediction of  extrem temperature events like cold and heat waves. To do so, we added some biased experts of the Météo-France 35-member ensemble forecast (PEARP) to the set of models. We also tried out the SMH (Mourtada et al. 2017) algorithm which fits the sleeping experts framework.