Time Series Source Separation with Slow Flows

• URL: http://arxiv.org/abs/2007.10182v1
• Date: Mon, 20 Jul 2020 15:15:27 GMT
• Title: Time Series Source Separation with Slow Flows
• Authors: Edouard Pineau, S\'ebastien Razakarivony, Thomas Bonald
• Abstract summary: We show that slow feature analysis (SFA) 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.
• Score: 5.953590600890215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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