  We develop the setting of sequential prediction based on shifting experts and
on a "smooth" version of the method of specialized experts. To aggregate
experts predictions, we use the AdaHedge algorithm, which is a version of the
Hedge algorithm with adaptive learning rate, and extend it by the
meta-algorithm Fixed Share. Due to this, we combine the advantages of both
algorithms: (1) we use the shifting regret which is a more optimal
characteristic of the algorithm; (2) regret bounds are valid in the case of
signed unbounded losses of the experts. Also, (3) we incorporate in this scheme
a "smooth" version of the method of specialized experts which allows us to make
more flexible and accurate predictions. All results are obtained in the
adversarial setting -- no assumptions are made about the nature of data source.
We present results of numerical experiments for short-term forecasting of
electricity consumption based on a real data.
