Recurrent Neural Networks for Multivariate Time Series with Missing Values

Understand

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.
• There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance.
• In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts.