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We consider the setting of sequential prediction of arbitrary sequences basedon specialized experts. We first provide a review of the relevant literatureand present two theoretical contributions: a general analysis of the specialistaggregation rule of Freund et al. (1997) and an adaptation of fixed-share rulesof Herbster and Warmuth (1998) in this setting. We then apply these rules tothe sequential short-term (one-day-ahead) forecasting of electricityconsumption; to do so, we consider two data sets, a Slovakian one and a Frenchone, respectively concerned with hourly and half-hourly predictions. We followa general methodology to perform the stated empirical studies and detail inparticular tuning issues of the learning parameters. The introduced aggregationrules demonstrate an improved accuracy on the data sets at hand; theimprovements lie in a reduced mean squared error but also in a more robustbehavior with respect to large occasional errors.

Forecasting electricity consumption by aggregating specialized experts