BRNN-GAN: generative adversarial networks with bi-directional recurrent neural networks for multivariate time series imputation

Wu, Z., Ma, C., Shi, X., Wu, L., Zhang, D., Tang, Y. and Stojmenovic, M. (2021) BRNN-GAN: generative adversarial networks with bi-directional recurrent neural networks for multivariate time series imputation. In: 2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS), Beijing. China, 14-16 Dec 2021, pp. 217-224. ISBN 9781665408783 (doi: 10.1109/ICPADS53394.2021.00033)

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).

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Tang, Dr Yutian
• Authors: Wu, Z., Ma, C., Shi, X., Wu, L., Zhang, D., Tang, Y., and Stojmenovic, M.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 2690-5965
• ISBN: 9781665408783
• Published Online: 03 May 2022