Missing value imputation in multivariate time series with end-to-end generative adversarial networks

Abstract

Missing values are inherent in multivariate time series because of multiple reasons, such as collection errors, which deteriorate the performance of follow-up analytic applications on the multivariate time series. Numerous missing value imputation methods have been proposed to mitigate the influence of missing values on multivariate time series analysis. Recently, inspired by the success of generative adversarial networks (GANs) in image generation, the GAN-2-Stage has been used to address the imputation problem with the generative model. Specifically, GAN-2-Stage employs GANs to impute the missing values. However, an extra phase is required to optimize the input random “noise” of the generator. In addition, the imputed values can be very different from real values because of the difficulty in training a GAN and the unstable generation process. Therefore, this paper proposes an end-to-end model to impute the missing values in a multivariate time series. Specifically, we introduce an encoder network into the standard GAN architecture that eliminates the input optimization phase in the GAN-2-Stage. Our generator utilizes real data during training to force the imputed values to be close to the real ones. Experiments on three real-world multivariate time series datasets demonstrate that the proposed model outperforms state-of-the-art methods in imputation tasks and downstream applications, including classification and regression.