  We propose Feature-aligned N-BEATS as a domain-generalized time series
forecasting model. It is a nontrivial extension of N-BEATS with doubly residual
stacking principle (Oreshkin et al.[42]) into a representation learning
framework. In particular, it revolves around marginal feature probability
measures induced by the intricate composition of residual and feature
extracting operators of N-BEATS in each stack and aligns them stack-wisely via
an approximate of an optimal transport distance referred to as the Sinkhorn
divergence. The training loss consists of an empirical risk minimization from
multiple source domains, i.e., forecasting loss, and an alignment loss
calculated with the Sinkhorn divergence, which allows the model to learn
invariant features stack-wisely across multiple source data sequences while
retaining N-BEATS's interpretable design and forecasting power. Comprehensive
experimental evaluations with ablation studies are provided and the
corresponding results demonstrate the proposed model's forecasting and
generalization capabilities.

Machine Learning;Artificial Intelligence;Optimization and Control;Probability