标题： 通过聚合专业专家来预测电力消耗 显示英文标题

标题： Forecasting electricity consumption by aggregating specialized experts

摘要： 我们考虑基于专门专家的任意序列顺序预测设置。首先，我们回顾相关文献，并提出两个理论贡献：分析Freund等人（1997）的专家聚合规则，以及Herbster和Warmuth（1998）固定份额规则在此设置中的适应性。然后，我们将这些规则应用于电力消耗的顺序短期（提前一天）预测；为此，我们考虑两个数据集，一个斯洛伐克的和一个法国的，分别涉及每小时和半小时的预测。我们遵循一般方法进行所述的经验研究，并详细讨论学习参数的调优问题。引入的聚合规则在手头的数据集上展示了更高的准确性；改进体现在均方误差减少以及对大偶发错误的更稳健行为。 显示英文摘要

摘要： 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.