Time Series Source Separation with Slow Flows
Machine Learning
2020-07-21 v1 Machine Learning
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.
Cite
@article{arxiv.2007.10182,
title = {Time Series Source Separation with Slow Flows},
author = {Edouard Pineau and Sébastien Razakarivony and Thomas Bonald},
journal= {arXiv preprint arXiv:2007.10182},
year = {2020}
}
Comments
INNF+ Workshop, ICML 2020