Over the last 15 years, studies on hierarchical forecasting have moved away from single-level approaches towards proposing linear combination approaches across multiple levels of the hierarchy. Such combinations offer coherent reconciled forecasts, improved forecasting performance and aligned decision-making. This paper proposes a novel hierarchical forecasting approach based on machine learning. The proposed method allows for non-linear combinations of the base forecasts, thus being more general than linear approaches. We structurally combine the objectives of improved post-sample empirical forecasting accuracy and coherence. Due to its non-linear nature, our approach selectively combines the base forecasts in a direct and automated way without requiring that the complete information must be used for producing reconciled forecasts for each series and level. The proposed method is evaluated both in terms of accuracy and bias using two different data sets coming from the tourism and retail industries. Our results suggest that the proposed method gives superior point forecasts than existing approaches, especially when the series comprising the hierarchy are not characterized by the same patterns.

在过去的 15 年中，关于分层预测的研究已经从单层方法转向跨层次结构的多个层级提出线性组合方法。这种组合提供连贯一致的预测、改进的预测性能和一致的决策。本文提出了一种基于机器学习的新型分层预测方法。所提出的方法允许基础预测的非线性组合，因此比线性方法更通用。我们在结构上结合了提高样本后经验预测准确性和一致性的目标。由于其非线性特性，我们的方法以直接和自动化的方式选择性地组合基础预测，而无需必须使用完整的信息来为每个系列和级别生成协调的预测。所提出的方法使用来自旅游和零售业的两个不同数据集在准确性和偏差方面进行评估。我们的结果表明，所提出的方法提供了比现有方法更好的点预测，尤其是当包含层次结构的系列不具有相同模式时。