论文标题
时间序列源分离与慢流量
Time Series Source Separation with Slow Flows
论文作者
论文摘要
在本文中，我们显示了一种常用时间序列分解方法缓慢的特征分析（SFA）自然拟合到基于流的模型（FBM）框架中，这是一种可逆的神经潜在变量模型。在盲目分离方面的最新进展基础上，我们表明这种拟合使得时间序列的分解可识别。
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.
