Transformer-GRU-GAN Based Data Restoration Approach

Abstract

The integrity of data greatly affects its quality, and thus its asset value and also the accuracy of data analysis tasks based on it. However, in reality, there are often various factors that cause data deficiency. Although many scholars have made researches on this problem, most of the studies have limited accuracy. Therefore, this paper proposes a transformer-GRU based GAN, which first learns the potential distribution of real data through generator formed by stacked encoder of transformer to generate the complementary data, and then inputs the complementary data into the discriminator based on GRU for discrimination. Finally, it trains the model through the adversarial game between generator and discriminator. Comparison experiments are carried out based on the sales data of a company, and it can be seen that the evaluation indicators of the method proposed in this paper are superior to those of the compared methods, proving that the missing data recovery method proposed in this paper has better accuracy.