サインイン
インサイト - Time Series Generation
Generating Sequential Data via the Path Characteristic Function of Measures on the Path Space
The authors propose a novel generative adversarial network (PCF-GAN) that incorporates the path characteristic function (PCF) as the principled representation of time series distribution into the discriminator to enhance its generative performance. The PCF-GAN also integrates an auto-encoder structure to enable simultaneous generation and reconstruction of complex time series data.
Diffusion-TS: Interpretable Diffusion for General Time Series Generation at ICLR 2024
Diffusion-TS proposes an interpretable diffusion framework for generating high-quality time series data, showcasing state-of-the-art results in various analyses.
