22: Enhancing Time Series Forecasting with Diffusion Models and Conformal Prediction

Music City Center 

Accurate time series predictions are crucial across various scientific fields. Traditional statistical methods, such as autoregressive integrated moving average (ARIMA) models, have been widely used but often rely on assumptions of stationarity and linearity, limiting their ability to capture complex real-world patterns. To overcome these limitations, this study introduces novel methods for point forecasting and uncertainty quantification. Generative diffusion models are employed alongside a conformal prediction-based calibration method to enhance the reliability of prediction intervals. The effectiveness of this approach is demonstrated through simulations and an application to electricity load forecasting. The results contribute broadly to time series analysis by improving predictive accuracy and ensuring robust uncertainty quantification.

Generative models

Diffusion models

Conformal prediction

Time series analysis

Uncertainty quantification 

ENAR