Hierarchical Neural Additive Models for Interpretable Demand Forecasts
Leif Feddersen Catherine Cleophas
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.