TimeAutoDiff: Combining Autoencoder and Diffusion model for time series tabular data synthesizing

This paper combines the ideas of the variational auto-encoder and the denoising diffusion probabilistic model to generate synthetic time series tabular data with heterogenous features, enabling scenario exploration across multiple scientific and engineering domains.