MiT-WGAN: Financial Time-Series Generation GAN Based on Multi-Convolution Dynamic Fusion and iTransformer

With the rapid growth of FinTech, time-series data has become pervasive in financial markets. However, the nonstationarity, high noise levels, and complex temporal dependencies of financial data pose significant challenges to the efficacy and stability of standard generative models. To overcome these limitations, we propose MiT-WGAN, a gradient-penalized Wasserstein generative adversarial network that integrates a multi-convolutional dynamic fusion (MCDF) module in parallel with an enhanced Transformer (iTransformer) to jointly capture local patterns and long-range dependencies. We evaluate MiT-WGAN on S&P 500 stock trading data using a comprehensive set of baseline models and evaluation metrics. Experimental results demonstrate that MiT-WGAN achieves superior sample quality, better preservation of statistical properties, and improved training stability, confirming its effectiveness for financial time-series modeling.