Informer-WGAN: High Missing Rate Time Series Imputation Based on Adversarial Training and a Self-Attention Mechanism

Abstract

Missing observations in time series will distort the data characteristics, change the dataset expectations, high-order distances, and other statistics, and increase the difficulty of data analysis. Therefore, data imputation needs to be performed first. Generally, data imputation methods include statistical imputation, regression imputation, multiple imputation, and imputation based on machine learning methods. However, these methods currently have problems such as insufficient utilization of time characteristics, low imputation efficiency, and poor performance under high missing rates. In response to these problems, we propose the informer-WGAN, a network model based on adversarial training and a self-attention mechanism. With the help of the discriminator network and the random missing rate training method, the informer-WGAN can efficiently solve the problem of multidimensional time series imputation. According to the experimental results under different missing rates, the informer-WGAN model achieves better imputation results than the original informer on two datasets. Our model also shows excellent performance on time series imputation of the key parameters of a spacecraft control moment gyroscope (CMG).