Generative diffusion models have shown promise in creating realistic data, but prior work has not fully addressed the unique temporal dependencies in financial time series. In this work, we introduce Multiscale Spatial–Temporal Dynamics Diffusion (MSSTD-Diff), a new approach to generate high-quality synthetic multivariate financial time series data. MSSTD-Diff uses advanced temporal encoding and multiscale data representation to capture complex patterns and relationships in financial markets over time. Unlike standard diffusion models, we use a one-step generation process and adjust the training to emphasize these temporal relationships. We evaluate the model using moments, autocorrelation, correlation, distributional properties, and an explainability index to assess how well the synthetic data mimics real market behavior. Our experiments show that MSSTD-Diff outperforms previous diffusion-based methods in reproducing the temporal dynamics of financial time series. These results suggest that our approach can produce higher-quality synthetic financial data, benefiting applications such as risk modeling, portfolio optimization, and algorithmic trading.
Wang et al. (Thu,) studied this question.