English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose Feature-aligned N-BEATS as a domain-generalized time seriesforecasting model. It is a nontrivial extension of N-BEATS with doubly residualstacking principle (Oreshkin et al.[42]) into a representation learningframework. In particular, it revolves around marginal feature probabilitymeasures induced by the intricate composition of residual and featureextracting operators of N-BEATS in each stack and aligns them stack-wisely viaan approximate of an optimal transport distance referred to as the Sinkhorndivergence. The training loss consists of an empirical risk minimization frommultiple source domains, i.e., forecasting loss, and an alignment losscalculated with the Sinkhorn divergence, which allows the model to learninvariant features stack-wisely across multiple source data sequences whileretaining N-BEATS's interpretable design and forecasting power. Comprehensiveexperimental evaluations with ablation studies are provided and thecorresponding results demonstrate the proposed model's forecasting andgeneralization capabilities.

Feature-aligned N-BEATS with Sinkhorn divergence