We introduce a dynamic approach to probabilistic forecast reconciliation at scale. Our model differs from the existing literature in this area in several important ways. Firstly we explicitly allow the weights allocated to the base forecasts in forming the combined, reconciled forecasts to vary over time. Secondly we drop the assumption, near ubiquitous in the literature, that in-sample base forecasts are appropriate for determining these weights, and use out of sample forecasts instead. Most existing probabilistic reconciliation approaches rely on time consuming sampling based techniques, and therefore do not scale well (or at all) to large data sets. We address this problem in two main ways, firstly by utilising a closed from estimator of covariance structure appropriate to hierarchical forecasting problems, and secondly by decomposing large hierarchies in to components which can be reconciled separately.

翻译：本文提出了一种可扩展的概率预测动态协调方法。我们的模型在多个重要方面区别于该领域的现有文献。首先，我们明确允许在形成组合协调预测时，分配给基础预测的权重随时间动态变化。其次，我们摒弃了文献中几乎普遍存在的假设——即样本内基础预测适用于确定这些权重，转而采用样本外预测。现有的大多数概率协调方法依赖于耗时的基于采样的技术，因此难以（或完全无法）扩展到大型数据集。我们主要通过两种方式解决该问题：一是采用适用于层次化预测问题的闭式协方差结构估计器；二是将大型层次结构分解为可独立协调的组件。