Generative Time Series

Generative time series modeling focuses on creating realistic synthetic time series data using machine learning techniques. Current research emphasizes developing novel generative models, including those based on neural ordinary differential equations, transformers, and diffusion processes, often coupled with techniques like reservoir computing and autoencoders, to improve data generation quality and assess it using metrics like the Fr´echet Fourier-transform Auto-encoder Distance (FFAD). These advancements are significant for addressing data scarcity, privacy concerns, and augmenting existing datasets in diverse fields like healthcare, finance, and brain imaging analysis, ultimately improving the performance of downstream tasks such as disease prediction and trajectory forecasting. The development of unified multi-task models capable of handling various time series tasks within a single framework is also a key area of focus.