BRITS: Bidirectional Recurrent Imputation for Time Series

TL;DR

BRITS is a novel bidirectional recurrent neural network method that effectively imputes missing values in time series data without assuming linear dynamics, outperforming existing methods in various real-world datasets.

Contribution

It introduces a data-driven, flexible RNN-based approach for missing value imputation in time series, capable of handling nonlinear dynamics and multiple correlated missing values.

Findings

Outperforms state-of-the-art imputation methods.

Improves classification/regression accuracy on real datasets.

Handles nonlinear dynamics and multiple missing values effectively.

Abstract

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.