Title: Addressing Spectral Energy Imbalance in Time-Series Forecasting with Gini-Guided Progressive Frequency Extraction
Primary area: learning on time series and dynamical systems
Keywords: Time series, Frequency, Spectral Energy Imbalance, Gini

Abstract: Time-series forecasting has recently seen growing interest, with increasing attention to frequency-domain representations.
Real-world time-series often exhibit spectral distributions in which some frequency components have disproportionately large amplitudes.
Since larger amplitudes correspond to higher energy, these components dominate the total energy.
Such an imbalance biases models toward high-energy frequency components, preventing them from learning low-energy components, thereby harming generalization.
We propose GiPFE (Gini-guided Progressive Frequency Extraction), a model-agnostic framework that progressively extracts high-energy frequency components from time-series.
This progressive extraction is crucial because even after the strongest components are removed, the remaining parts may still contain relatively strong frequencies that sustain the imbalance.
GiPFE measures the degree of spectral imbalance in each channel using the Gini coefficient and dynamically adjusts the number of components extracted at each stage to achieve precise extraction.
By gradually separating dominant high-energy patterns, GiPFE prevents a single predictor from being dominated by a few strong components, allowing auxiliary lightweight heads to capture simple high-energy patterns while the backbone focuses on the remaining complex low-energy structures.
Experiments on five real-world datasets with four backbone models demonstrate that GiPFE consistently improves forecasting performance across diverse architectures and domains.