Time series imputation with GAN inversion and decay connection

Abstract

Time series imputation is essential for real-world applications. Though the emergence of Generative Adversarial Networks (GANs) and Graph Convolution Networks (GCNs) provides more possibilities to improve imputation performance, how to achieve the optimal latent code and precisely model the properties of incomplete time series remain a challenge. In GAN-based methods, an effective latent code of incomplete time series is necessary for precise reconstruction. To acquire the optimal latent code, we introduce GAN inversion to invert the input to the latent space of a pretrained GAN. The inverted latent code contains rich properties of original observations and thus can better reconstruct the target sample. To model the temporal irregularity due to the presence of missing values, the decay connection is exploited to quantify the influence that dependencies between adjacent observations should decrease as the time lags between them increase. We incorporate the quantification into the adjacent matrix of the GCN to better aggregate adjacent information of incomplete time series. With the adoption of decay connection, the resulting latent code through GAN inversion can further produce faithful reconstruction. Quantitative and qualitative experiments conducted on several time series datasets show that our proposal achieves state-of-the-art or competitive imputation performance.