Time Series Source Separation with Slow Flows
Résumé
In this paper, we show that slow feature analysis (SFA), a common time series decomposition method, naturally fits into the flow-based models (FBM) framework, a type of invertible neural latent variable models. Building upon recent advances on blind source separation, we show that such a fit makes the time series decomposition identifiable.
Domaines
Apprentissage [cs.LG]| Origine | Fichiers produits par l'(les) auteur(s) |
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