Time series are ubiquitous in many classification/regression applications. However, the time series data in real applications may contain many missing values. Hence, given multiple (possibly correlated) time series data, it is important to fill in missing values and at the same time to predict their class labels. Existing imputation methods often impose strong assumptions of the underlying data generating process, such as linear dynamics in the state space. In this paper, we propose a novel method, called BRITS, based on recurrent neural networks for missing value imputation in time series data.

Time series are ubiquitous in many classification/regression applications. However, the time series data in real applications may contain many missing values. Hence, given multiple (possibly correlated) time series data, it is important to fill in missing values and at the same time to predict their class labels. Existing imputation methods often impose strong assumptions of the underlying data generating process, such as linear dynamics in the state space. In this paper, we propose a novel method, called BRITS, based on recurrent neural networks for missing value imputation in time series data.