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

研究了基于专家的任意序列的顺序预测，在固定分享规则和与其他学习规则的相关性方面提出了两个理论结果，并将这些规则应用于电力消费的顺序短期预测。通过在斯洛伐克和法国的数据集上进行实证研究，实验结果表明引入的聚合规则在减小方差误差和改进鲁棒性方面能够提高数据集的精确度。

聚合专业专家预测电力消耗