FDF: Flexible Decoupled Framework for Time Series Forecasting with Conditional Denoising and Polynomial Modeling

A novel flexible decoupled framework (FDF) that learns high-quality time series representations for enhanced forecasting performance and incorporates a Polynomial Trend Module (PTM) to effectively capture the smooth trend component, thereby enhancing the model’s ability to represent temporal dependencies.