Time Series Diffusion

Time series diffusion models leverage the power of diffusion processes to generate, forecast, and analyze sequential data. Current research focuses on improving model architectures, such as U-Nets with attention mechanisms, and exploring different prior distributions and conditioning methods to enhance generation quality and control. These advancements are impacting various fields, including signal processing, financial modeling, and healthcare, by enabling more accurate predictions, efficient data augmentation, and improved understanding of complex temporal dynamics. Furthermore, research is extending these methods to handle mixed-data types and operate effectively in both time and frequency domains.