  Time series are widely used as signals in many classification/regression
tasks. It is ubiquitous that time series contains many missing values. Given
multiple correlated time series data, how to fill in missing values and 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 BRITS, a novel method based on
recurrent neural networks for missing value imputation in time series data. Our
proposed method directly learns the missing values in a bidirectional recurrent
dynamical system, without any specific assumption. The imputed values are
treated as variables of RNN graph and can be effectively updated during the
backpropagation.BRITS has three advantages: (a) it can handle multiple
correlated missing values in time series; (b) it generalizes to time series
with nonlinear dynamics underlying; (c) it provides a data-driven imputation
procedure and applies to general settings with missing data.We evaluate our
model on three real-world datasets, including an air quality dataset, a
health-care data, and a localization data for human activity. Experiments show
that our model outperforms the state-of-the-art methods in both imputation and
classification/regression accuracies.
