Time Series Source Separation with Slow Flows

Edouard Pineau; Sébastien Razakarivony; Thomas Bonald

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arXiv · 2007.10182

Time Series Source Separation with Slow Flows

Edouard Pineau·Sébastien Razakarivony·Thomas Bonald

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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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Edouard Pineau, Sébastien Razakarivony, Thomas Bonald. 2020-07-20. Time Series Source Separation with Slow Flows. https://arxiv.org/abs/2007.10182

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