Many sectors nowadays require accurate and coherent predictions across their organization to effectively operate. Otherwise, decision-makers would be planning using disparate views of the future, resulting in inconsistent decisions across their sectors. To secure coherency across hierarchies, recent research has put forward hierarchical learning, a coherency-informed hierarchical regressor leveraging the power of machine learning thanks to a custom loss function founded on optimal reconciliation methods. While promising potentials were outlined, results exhibited discordant performances in which coherency information only improved hierarchical forecasts in one setting. This work proposes to tackle these obstacles by investigating custom neural network designs inspired by the topological structures of hierarchies. Results unveil that, in a data-limited setting, structural models with fewer connections perform overall best and demonstrate the coherency information value for both accuracy and coherency forecasting performances, provided individual forecasts were generated within reasonable accuracy limits. Overall, this work expands and improves hierarchical learning methods thanks to a structurally-scaled learning mechanism extension coupled with tailored network designs, producing a resourceful, data-efficient, and information-rich learning process.

翻译：如今，许多领域需要在其组织内部实现准确且一致的预测，以便有效运营。否则，决策者将依据不同的未来愿景进行规划，导致部门间决策不一致。为确保层级间的一致性，近期研究提出了分层学习——一种利用最优协调方法定制损失函数、借助机器学习力量的一致性感知分层回归器。尽管指出了其潜在优势，但结果却显示出不一致的表现，即一致性信息仅在一项设定中改善了层级预测。本研究旨在通过探究受层级拓扑结构启发的定制化神经网络设计，来应对这些挑战。结果表明，在数据有限的环境中，连接较少的结构化模型整体表现最佳，并证实了在个体预测精度合理范围内，一致性信息对预测准确性和一致性的双重价值。总体而言，本研究通过引入结构化尺度化学习机制扩展与定制化网络设计，拓展并改进了分层学习方法，形成了一种资源高效、数据节约且信息丰富的学习过程。