Title: Adaptive Energy Amplification for Robust Time Series Forecasting
Primary area: learning on time series and dynamical systems
Keywords: Time Series Forecasting; Frequency-Domain Learning; Adaptive Energy Amplification; Spectral Bias

Abstract: Deep learning models for time series forecasting often exhibit a spectral bias, prioritizing high-energy, low-frequency components while underfitting predictive but low-energy, high-frequency signals. 
Existing efforts attempt to correct this by amplifying high-frequency components but suffer from indiscriminate amplification, enhancing both meaningful signals and task-irrelevant noise, which destabilizes training and impairs generalization. 
To address this, we propose AEA (Adaptive Energy Amplification), a novel framework that reframes the problem as one of adaptive signal enhancement. 
AEA introduces two synergistic innovations: (1) a Spectral Mirroring mechanism that constructs a phase-preserving, low-frequency surrogate to guide targeted, distortion-free amplification of high-frequency signals; 
and (2) a lightweight Differential Embedding module that operates in a latent space to adaptively suppress common-mode noise. 
By decoupling signal amplification from noise suppression, AEA selectively enhances only informative features. 
Extensive experiments on eight benchmark datasets show that our model-agnostic framework consistently improves the forecasting performance of four state-of-the-art backbones, while significantly enhancing training stability and generalization. 
The code repository is available at https://anonymous.4open.science/r/AEA-685E/.