  In this article, we aim at improving the prediction of expert aggregation by
using the underlying properties of the models that provide expert predictions.
We restrict ourselves to the case where expert predictions come from Kalman
recursions, fitting state-space models. By using exponential weights, we
construct different algorithms of Kalman recursions Aggregated Online (KAO)
that compete with the best expert or the best convex combination of experts in
a more or less adaptive way. We improve the existing results on expert
aggregation literature when the experts are Kalman recursions by taking
advantage of the second-order properties of the Kalman recursions. We apply our
approach to Kalman recursions and extend it to the general adversarial expert
setting by state-space modeling the errors of the experts. We apply these new
algorithms to a real dataset of electricity consumption and show how it can
improve forecast performances comparing to other exponentially weighted average
procedures.
