Trend-Aware Data Imputation Based on Generative Adversarial Network for Time Series

Abstract

To solve the problems of generative adversarial network (GAN)-based imputation method for time series, which are ignoring the implied trends in data and using multi-stage training that may lead to high training complexity, this article proposes a trend-aware data imputation method based on GAN (TrendGAN). It implements an end-to-end training using de-noising auto-encoder (DAE). It also uses bidirectional gated recurrent unit (Bi-GRU) in the generator model to consider the bi-directional characteristics and supplement the features lost by de-noising auto-encoder and improves the discriminator's ability using Bi-GRU and hint vector. The authors conducted experiments on four real datasets. The results showed that all components introduced into the method contribute to enhancing the imputation accuracy, and the MSE values of TrendGAN are much lower than those of baseline methods when dealing with time series with random and continuous missing patterns. That is, TrendGAN is suitable for data imputation in complex scenarios with two missing patterns coexist, such as electric power and transportation.