Rolling Diffusion

Rolling diffusion is a technique that modifies standard diffusion models by incorporating a sliding window approach to handle sequential data, assigning increasing noise to later time points to reflect growing uncertainty. Current research focuses on applying this method to diverse time-series problems, including video prediction, fluid dynamics forecasting, and internet traffic prediction, often comparing its performance against traditional models like ARIMA, SARIMA, and exponential smoothing methods. The improved accuracy demonstrated in these applications highlights the potential of rolling diffusion to enhance forecasting and prediction in various fields, particularly where temporal dynamics are complex and uncertainty increases over time.