论文标题
卡尔曼递归在线汇总
Kalman Recursions Aggregated Online
论文作者
论文摘要
在本文中，我们旨在通过使用提供专家预测的模型的基本属性来改善专家聚合的预测。我们将自己限制在专家预测来自Kalman递归，适合状态空间模型的情况下。通过使用指数级的权重，我们构建了在线汇总（KAO）的不同算法，该算法以或多或少的适应性方式与最佳专家或最佳专家组合竞争。当专家是Kalman递归时，我们通过利用Kalman递归的二阶属性来改善专家聚合文献的现有结果。我们将我们的方法应用于卡尔曼递归，并通过对专家的错误进行建模，将其扩展到一般对抗专家环境。我们将这些新算法应用于电力消耗的真实数据集，并显示与其他指数加权平均程序相比，它如何改善预测性能。
In this article, we aim at improving the prediction of expert aggregation by using the underlying properties of the models that provide expert predictions. We restrict ourselves to the case where expert predictions come from Kalman recursions, fitting state-space models. By using exponential weights, we construct different algorithms of Kalman recursions Aggregated Online (KAO) that compete with the best expert or the best convex combination of experts in a more or less adaptive way. We improve the existing results on expert aggregation literature when the experts are Kalman recursions by taking advantage of the second-order properties of the Kalman recursions. We apply our approach to Kalman recursions and extend it to the general adversarial expert setting by state-space modeling the errors of the experts. We apply these new algorithms to a real dataset of electricity consumption and show how it can improve forecast performances comparing to other exponentially weighted average procedures.
