Recurrent Neural Networks for Multivariate Time Series with Missing Values

6 Jun 2016Zhengping CheSanjay PurushothamKyunghyun ChoDavid SontagYan Liu

Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness... (read more)

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