GP-VAE: Deep Probabilistic Time Series Imputation
GP-VAE: Deep Probabilistic Time Series Imputation
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发表时间：
2019-07
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通讯作者：
Vincent Fortuin;Dmitry Baranchuk;Gunnar Rätsch;S. Mandt
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作者：
Vincent Fortuin;Dmitry Baranchuk;Gunnar Rätsch;S. Mandt
具有缺失值的多变量时间序列在医疗保健和金融等领域很常见，并且多年来在数量和复杂性方面都有所增长。这就提出了一个问题，深度学习方法是否能在这个领域胜过经典的数据输入方法。然而，深度学习的幼稚应用在给出可靠的置信度估计和缺乏可解释性方面存在不足。提出了一种新的深度序列潜变量模型，用于降维和数据输入。我们的建模假设是简单的和可解释的：高维时间序列有一个低维的表示，它根据高斯过程在时间上平滑地演变。在存在缺失数据的情况下，使用具有新颖结构化变分近似的VAE方法实现了非线性降维。我们证明，我们的方法在计算机视觉和医疗保健领域的高维数据上优于几种经典的和基于深度学习的数据输入方法，同时还提高了输入的平滑性并提供了可解释的不确定性估计。
Multivariate time series with missing values are common in areas such as healthcare and finance, and have grown in number and complexity over the years. This raises the question whether deep learning methodologies can outperform classical data imputation methods in this domain. However, naive applications of deep learning fall short in giving reliable confidence estimates and lack interpretability. We propose a new deep sequential latent variable model for dimensionality reduction and data imputation. Our modeling assumption is simple and interpretable: the high dimensional time series has a lower-dimensional representation which evolves smoothly in time according to a Gaussian process. The non-linear dimensionality reduction in the presence of missing data is achieved using a VAE approach with a novel structured variational approximation. We demonstrate that our approach outperforms several classical and deep learning-based data imputation methods on high-dimensional data from the domains of computer vision and healthcare, while additionally improving the smoothness of the imputations and providing interpretable uncertainty estimates.