In this study, we propose Feature-aligned N-BEATS as a domain generalization
model for univariate time series forecasting problems. The proposed model is an
extension of the doubly residual stacking architecture of N-BEATS (Oreshkin et
al. [34]) into a representation learning framework. The model is a new
structure that involves marginal feature probability measures (i.e.,
pushforward measures of multiple source domains) induced by the intricate
composition of residual operators of N-BEATS in each stack and aligns them
stack-wise via an entropic regularized Wasserstein distance referred to as the
Sinkhorn divergence (Genevay et al. [14]). The loss function consists of a
typical forecasting loss for multiple source domains and an alignment loss
calculated with the Sinkhorn divergence, which allows the model to learn
invariant features stack-wise across multiple source data sequences while
retaining N-BEATS's interpretable design. We conduct a comprehensive
experimental evaluation of the proposed approach and the results demonstrate
the model's forecasting and generalization capabilities in comparison with
methods based on the original N-BEATS