Demand forecasts are the crucial basis for numerous business decisions, ranging from inventory management to strategic facility planning. While machine learning (ML) approaches offer accuracy gains, their interpretability and acceptance are notoriously lacking. Addressing this dilemma, we introduce Hierarchical Neural Additive Models for time series (HNAM). HNAM expands upon Neural Additive Models (NAM) by introducing a time-series specific additive model with a level and interacting covariate components. Covariate interactions are only allowed according to a user-specified interaction hierarchy. For example, weekday effects may be estimated independently of other covariates, whereas a holiday effect may depend on the weekday and an additional promotion may depend on both former covariates that are lower in the interaction hierarchy. Thereby, HNAM yields an intuitive forecasting interface in which analysts can observe the contribution for each known covariate. We evaluate the proposed approach and benchmark its performance against other state-of-the-art machine learning and statistical models extensively on real-world retail data. The results reveal that HNAM offers competitive prediction performance whilst providing plausible explanations.

翻译：需求预测是众多业务决策的关键基础，从库存管理到战略设施规划。虽然机器学习方法能提升准确性，但其可解释性和接受度却明显不足。针对这一困境，我们提出了用于时间序列的层次化神经加性模型（HNAM）。HNAM在神经加性模型（NAM）的基础上进行扩展，引入了一种包含水平项和交互协变量分量的时间序列专用加性模型。协变量交互仅允许按照用户指定的交互层次进行。例如，工作日效应可独立于其他协变量进行估计，而节假日效应可能依赖于工作日，额外的促销效应则可能同时依赖于层次中较低的前两个协变量。由此，HNAM提供了一种直观的预测框架，分析师能够观察每个已知协变量的贡献。我们在真实零售数据上对该方法进行了全面评估，并将其性能与其他先进的机器学习和统计模型进行了基准对比。结果表明，HNAM在提供合理解释的同时，具备具有竞争力的预测性能。