Hierarchical forecasting is a key problem in many practical multivariate forecasting applications - the goal is to simultaneously predict a large number of correlated time series that are arranged in a pre-specified aggregation hierarchy. The challenge is to exploit the hierarchical correlations to simultaneously obtain good prediction accuracy for time series at different levels of the hierarchy. In this paper, we propose a new approach for hierarchical forecasting based on decomposing the time series along a global set of basis time series and modeling hierarchical constraints using the coefficients of the basis decomposition for each time series. Unlike past methods, our approach is scalable at inference-time (forecasting for a specific time series only needs access to its own data) while (approximately) preserving coherence among the time series forecasts. We experiment on several publicly available datasets and demonstrate significantly improved overall performance on forecasts at different levels of the hierarchy, compared to existing state-of-the-art hierarchical reconciliation methods.

翻译：在许多实用的多变量预测应用中,等级预测是一个关键问题,目标是同时预测在预先指定的聚合等级结构中安排的大量相关时间序列,挑战在于利用等级关系,同时在等级结构的不同层次获得对时间序列的良好预测准确性。在本文中,我们提出了一个新的等级预测方法,根据时间序列按照一套全球基准时间序列进行分解,并利用每个时间序列的基础分解系数来模拟等级限制。与以往的方法不同,我们的方法在推论时间是可变的(预言某一具体时间序列只需要查阅自己的数据),同时(大约)保持时间序列预测的一致性。我们实验了几个公开的数据集,并表明与现有的最先进的等级调和方法相比,不同层次层次的预测总体业绩显著改善。