A novel framework for hierarchical forecast updating is presented, addressing a critical gap in the forecasting literature. By assuming a temporal hierarchy structure, the innovative approach extends hierarchical forecast reconciliation to effectively manage the challenge posed by partially observed data. This crucial extension allows, in conjunction with real-time data, to obtain updated and coherent forecasts across the entire temporal hierarchy, thereby enhancing decision-making accuracy. The framework involves updating base models in response to new data, which produces revised base forecasts. A subsequent pruning step integrates the newly available data, allowing for the application of any forecast reconciliation method to obtain fully updated reconciled forecasts. Additionally, the framework not only ensures coherence among forecasts but also improves overall accuracy throughout the hierarchy. Its inherent flexibility and interpretability enable users to perform hierarchical forecast updating concisely. The methodology is extensively demonstrated in a simulation study with various settings and comparing different data-generating processes, hierarchies, and reconciliation methods. Practical applicability is illustrated through two case studies in the energy sector, energy generation and solar power data, where the framework yields superior results compared to base models that do not incorporate new data, leading to more precise decision-making outcomes.

翻译：本文提出了一种新颖的层次预测更新框架，弥补了预测文献中的一个关键空白。该方法通过假设时间层次结构，将层次预测协调技术扩展至部分观测数据的处理挑战中。这一关键扩展使得结合实时数据能够在整个时间层次上获得更新且协调一致的预测，从而提升决策准确性。该框架包含根据新数据更新基础模型以生成修正的基础预测，随后通过剪枝步骤整合新数据，从而可应用任意预测协调方法获得完全更新的协调预测。此外，该框架不仅保证了预测间的协调性，还提升了整个层次结构的整体预测精度。其固有的灵活性与可解释性使用户能够简洁地进行层次预测更新。本研究通过模拟实验在不同设定下，对比多种数据生成过程、层次结构及协调方法，全面验证了该方法的有效性。在能源领域的两个案例研究（能源发电与太阳能发电数据）中，该框架相比未整合新数据的基础模型展现出更优性能，实现了更精准的决策结果。