Hierarchical time-series forecasting (HTSF) is an important problem for many
real-world business applications where the goal is to simultaneously forecast
multiple time-series that are related to each other via a hierarchical
relation. Recent works, however, do not address two important challenges that
are typically observed in many demand forecasting applications at large
companies. First, many time-series at lower levels of the hierarchy have high
sparsity i.e., they have a significant number of zeros. Most HTSF methods do
not address this varying sparsity across the hierarchy. Further, they do not
scale well to the large size of the real-world hierarchy typically unseen in
benchmarks used in literature. We resolve both these challenges by proposing
HAILS, a novel probabilistic hierarchical model that enables accurate and
calibrated probabilistic forecasts across the hierarchy by adaptively modeling
sparse and dense time-series with different distributional assumptions and
reconciling them to adhere to hierarchical constraints. We show the scalability
and effectiveness of our methods by evaluating them against real-world demand
forecasting datasets. We deploy HAILS at a large chemical manufacturing company
for a product demand forecasting application with over ten thousand products
and observe a significant 8.5\% improvement in forecast accuracy and 23% better
improvement for sparse time-series. The enhanced accuracy and scalability make
HAILS a valuable tool for improved business planning and customer experience.

HAILS 是一种新颖的概率层次模型，通过自适应地对不同分布假设的稀疏和密集时间序列进行建模，以遵循层次约束，从而实现层次结构中的精确和校准的概率预测。通过在实际需求预测数据集上进行评估，我们展示了我们方法的可扩展性和有效性。我们在一家大型化工制造公司部署 HAILS 进行产品需求预测应用，涵盖了成千上万个产品，结果显示预测准确性提高了 8.5％，稀疏时间序列的改进提高了 23％。增强的准确性和可扩展性使 HAILS 成为改进商业规划和客户体验的有价值工具。