Self-Adaptive Scale Handling for Time Series Forecasting

A new module called Self-Adaptive Scale-handling (AS) addresses scale heterogeneity in time series forecasting. It uses Scale Calibrating and Scaling Selection to adaptively adjust scaling factors, preserving semantic discriminability and reducing inverse-scaling errors. Experiments on fund sales data show improved performance when integrated into existing forecasting models.