Time series data is collected in chronological order to represent how the collected data changes over time. This type of data is susceptible to interference from external factors that ultimately make the data missing. Missing data will cause the lack of some historical information, and it is not conducive to the development of downstream tasks. In recent years, Generative Adversarial Networks (GAN) GAN is widely used in image processing tasks and has achieved good results, which can also be applied to time series generation and interpolation tasks. We analyze the types of time series data missing, the structure of general GAN, and compare three models such as E2GAN, BIGAN and MBGAN, focusing on the comparison of the model composition of generator and discriminator, so as to provide ideas for the subsequent optimization application of GAN in time series data imputation.