Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement Yan Li, Xinjiang Lu

Time series forecasting has been a widely explored task of great importance in many applications. However, it is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series.