Optimizing GAN-Based Data Augmentation for Predictive Financial Analytics

Abstract

Deep Generative Adversarial Networks (GANs) have shown strong data enhancement and prediction capabilities in financial data modeling, and can effectively alleviate the scarcity, non-stationarity, and noise interference of financial time series data. This study focuses on the generation and prediction of financial data, using a variety of GAN variants, including standard GAN, WGAN, TimeGAN and their improved versions, and improves the authenticity and temporal consistency of generated data by introducing Wasserstein distance optimization, time series autoregression mechanism and Transformer structure. The experiment is based on the NASDAQ-100 dataset to evaluate the performance of different GAN variants in market volatility modeling, and uses statistical indicators such as mean maximum deviation (mMD) and KL divergence to measure data quality. The results show that the data generated by TimeGAN and WGAN + Transformer significantly improves the prediction accuracy while maintaining market characteristics. In addition, this study also analyzes the impact of data enhancement on LSTM and Transformer prediction models, proving that GAN-generated data can improve the stability of financial market trend prediction. The research results provide a new methodology for financial intelligent analysis and promote the application of GANs in the field of financial technology.