BRNN-GAN: Generative Adversarial Networks with Bi-directional Recurrent Neural Networks for Multivariate Time Series Imputation
Zejun Wu, Chao Ma, Xiaochuan Shi, Libing Wu, Dian Zhang, Yutian Tang, Miloš Stojmenović
Abstract
Missing values appearing in multivariate time series often prevent further and in-depth analysis in real-world applications. To handle those missing values, advanced multivariate time series imputation methods are expected to (1) consider bi-directional temporal correlations, (2) model cross-variable correlations, and (3) approximate original data's distribution. However, most of existing approaches are not able to meet all the three above-mentioned requirements. Drawing on advances in machine learning, we propose BRNN-GAN, a generative adversarial network with bi-directional RNN cells. The BRNN cell is designed to model bi-directional temporal and cross-variable correlations, and the GAN architecture is employed to learn original data's distribution. By conducting comprehensive experiments on two public datasets, the experimental results show that our proposed BRNN-GAN outperforms all the baselines in terms of achieving the lowest Mean Absolute Error (MAE).