FreqEvo: Enhancing Time Series Forecasting With Multi-Level Frequency Domain Feature Extraction

Time series forecasting faces significant challenges due to non-stationary components that obscure underlying patterns. While Transformer-based models are effective at capturing stationary components, they struggle with non-stationary dynamics and multivariate dependencies. In this paper, we propose FreqEvo, a lightweight Frequency Domain Feature Enhancement module for time series forecasting. FreqEvo progressively filters frequency components from high to low amplitude, ensuring the preservation of informative features while reducing noise. By integrating recursive Fourier-based residual modeling and cross-domain attention, FreqEvo effectively refines low-amplitude frequency features and stabilizes the embeddings, outperforming traditional low-pass filtering and random frequency selection methods in capturing both short-term and long-term dependencies. Experimental results on benchmark datasets demonstrate that FreqEvo outperforms state-of-the-art (SOTA) models and serves as a plug-and-play module to enhance existing Long-Term Sequence Forecasting (LSTF) models.