In several applications, there are hierarchically-organized time series that can be aggregated at various levels. In this paper, a novel Support Vector Regression approach is proposed for dealing with hierarchical time series forecasting. The main idea is to pool information across levels of hierarchy, preventing bottom-level series from deviate much with respect to the series at the upper levels. The reasoning behind this approach is to estimate robust bottom-level models that can deal with the intrinsic noise present at this level due to the lack of information. Two variants are presented: First, we solve a single optimization problem that constructs all the related regression functions together, relating the bottom level series with the root node, while the second variant pools relates the leaf nodes with their respective parent nodes. The proposed approach showed best performance when compared with the state of the art on hierarchical time series forecasting using well-known benchmark datasets.

在几个应用程序中，有可以在不同级别聚合的分层组织的时间序列。在本文中，提出了一种新的支持向量回归方法来处理分层时间序列预测。主要思想是在层次结构中汇集信息，防止底层系列相对于上层系列有太大偏差。这种方法背后的原因是估计可以处理由于缺乏信息而出现在该级别的固有噪声的稳健底层模型。提出了两个变体：首先，我们解决了一个单一的优化问题，该问题将所有相关的回归函数构建在一起，将底层序列与根节点相关联，而第二个变体池将叶节点与其各自的父节点相关联。