BRITS: Bidirectional Recurrent Imputation for Time Series

Our proposed method directly learns the missing values ina bidirectional recurrent dynamical system, without anyspecific assumption. The imputed values are treated as variables of RNN graph and can be effectively updated during backpropagation. BRITS hasthree advantages: (a)itcanhandle multiple correlated missing values intime series; (b) itgeneralizes totime 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 healthcare dataset, and a localization dataset for human activity. Experiments show that our model outperforms the state-of-the-art methods in both imputation and classification/regression.