Time Series Source Separation with Slow Flows
Edouard Pineau, Sébastien Razakarivony, Thomas Bonald · arXiv (Cornell University) · 2020
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.