Hierarchical time-series forecasting is essential for demand prediction across various industries. While machine learning models have obtained significant accuracy and scalability on such forecasting tasks, the interpretability of their predictions, informed by application, is still largely unexplored. To bridge this gap, we introduce a novel interpretability method for large hierarchical probabilistic time-series forecasting, adapting generic interpretability techniques while addressing challenges associated with hierarchical structures and uncertainty. Our approach offers valuable interpretative insights in response to real-world industrial supply chain scenarios, including 1) the significance of various time-series within the hierarchy and external variables at specific time points, 2) the impact of different variables on forecast uncertainty, and 3) explanations for forecast changes in response to modifications in the training dataset. To evaluate the explainability method, we generate semi-synthetic datasets based on real-world scenarios of explaining hierarchical demands for over ten thousand products at a large chemical company. The experiments showed that our explainability method successfully explained state-of-the-art industrial forecasting methods with significantly higher explainability accuracy. Furthermore, we provide multiple real-world case studies that show the efficacy of our approach in identifying important patterns and explanations that help stakeholders better understand the forecasts. Additionally, our method facilitates the identification of key drivers behind forecasted demand, enabling more informed decision-making and strategic planning. Our approach helps build trust and confidence among users, ultimately leading to better adoption and utilization of hierarchical forecasting models in practice.

翻译：层次化时间序列预测对于各行业的需求预测至关重要。尽管机器学习模型在此类预测任务中已获得显著的准确性与可扩展性，但其预测结果在应用场景中的可解释性仍很大程度上未被探索。为填补这一空白，我们提出了一种针对大规模层次化概率时间序列预测的新型可解释性方法，该方法在适配通用可解释性技术的同时，解决了与层次化结构及不确定性相关的挑战。我们的方法为现实工业供应链场景提供了有价值的解释性洞见，包括：1）层次结构中各时间序列及特定时间点外部变量的重要性；2）不同变量对预测不确定性的影响；3）针对训练数据集修改所引发预测变化的解释。为评估该可解释性方法，我们基于某大型化工企业解释上万种产品层次化需求的真实场景生成了半合成数据集。实验表明，我们的可解释性方法成功解释了最先进的工业预测方法，且解释准确率显著更高。此外，我们提供了多个真实案例研究，证明该方法能有效识别重要模式与解释，帮助利益相关者更好地理解预测结果。同时，我们的方法有助于识别预测需求背后的关键驱动因素，从而实现更明智的决策与战略规划。该方法有助于建立用户信任与信心，最终推动层次化预测模型在实践中得到更好的采纳与应用。