  The paper presents numerical experiments and some theoretical developments in
prediction with expert advice (PEA). One experiment deals with predicting
electricity consumption depending on temperature and uses real data. As the
pattern of dependence can change with season and time of the day, the domain
naturally admits PEA formulation with experts having different ``areas of
expertise''. We consider the case where several competing methods produce
online predictions in the form of probability distribution functions. The
dissimilarity between a probability forecast and an outcome is measured by a
loss function (scoring rule). A popular example of scoring rule for continuous
outcomes is Continuous Ranked Probability Score (CRPS). In this paper the
problem of combining probabilistic forecasts is considered in the PEA
framework. We show that CRPS is a mixable loss function and then the
time-independent upper bound for the regret of the Vovk aggregating algorithm
using CRPS as a loss function can be obtained. Also, we incorporate a
``smooth'' version of the method of specialized experts in this scheme which
allows us to combine the probabilistic predictions of the specialized experts
with overlapping domains of their competence.
