FSC: Bridging Periodic Patterns and Local Dynamics in Multivariate Time Series Forecasting

Multivariate time series forecasting is widely used in domains such as traffic, energy, and finance, where effective forecasting requires capturing both stable periodic patterns and rapidly evolving local dynamics. However, under distribution shifts, periodic priors learned from historical data can become mismatched with fast local dynamics, leading to prediction lag. To address this issue, we introduce Fast-to-Slow Correction (FSC), a lightweight and plug-and-play framework that can be seamlessly integrated into existing forecasting models. FSC explicitly decouples fast and slow features via parallel pathways, and employs a learnable refinement module to correct slow periodic features using fast local dynamics as adaptive references. This design effectively mitigates prediction lag in periodic modeling without modifying the backbone architecture. Extensive experiments on 12 real-world datasets show that FSC achieves state-of-the-art results on 9 datasets, with an average 2.51% MSE reduction over strong periodic forecasting methods. FSC also achieves these improvements with minimal computational overhead, introducing only $\sim$1.26M additional parameters.