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,许多部门需要准确和一致的预测,才能有效运作。否则,决策者将利用对未来的不同观点来规划未来,从而导致部门之间决策不一致。为了确保各等级之间的一致性,最近的研究提出了等级学习,一个对一致性有了解的等级递减者利用机器学习的动力,其原因是基于最佳调和方法的定制损失功能。虽然勾勒了有希望的潜力,但结果表现出不协调的性能,即一致性信息只改善一个环境的等级预测。这项工作提议通过调查由等级结构的地形结构所启发的定制神经网络设计来克服这些障碍。结果揭示,在数据有限的情况下,结构模型能够发挥整体的最佳作用,并展示出准确性和一致性预测业绩的连贯一致性信息价值,条件是个人预测是在合理的准确限度内产生的。总体而言,这项工作扩大并改进了等级学习方法,因为结构化学习机制的扩展加上定制的网络设计,产生了资源丰富、数据效率和信息丰富的学习过程。