FreDN: Spectral Disentanglement for Time Series Forecasting via Learnable Frequency Decomposition

• URL: http://arxiv.org/abs/2511.11817v1
• Date: Fri, 14 Nov 2025 19:13:24 GMT
• Title: FreDN: Spectral Disentanglement for Time Series Forecasting via Learnable Frequency Decomposition
• Authors: Zhongde An, Jinhong You, Jiyanglin Li, Yiming Tang, Wen Li, Heming Du, Shouguo Du,
• Abstract summary: We propose a learnable Frequency Disentangler module to separate trend and periodic components directly in the frequency domain.<n>We also propose a theoretically supported ReIm Block to reduce the complexity of complex-valued operations while maintaining performance.
• Score: 11.495360594018186
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series forecasting is essential in a wide range of real world applications. Recently, frequency-domain methods have attracted increasing interest for their ability to capture global dependencies. However, when applied to non-stationary time series, these methods encounter the $\textit{spectral entanglement}$ and the computational burden of complex-valued learning. The $\textit{spectral entanglement}$ refers to the overlap of trends, periodicities, and noise across the spectrum due to $\textit{spectral leakage}$ and the presence of non-stationarity. However, existing decompositions are not suited to resolving spectral entanglement. To address this, we propose the Frequency Decomposition Network (FreDN), which introduces a learnable Frequency Disentangler module to separate trend and periodic components directly in the frequency domain. Furthermore, we propose a theoretically supported ReIm Block to reduce the complexity of complex-valued operations while maintaining performance. We also re-examine the frequency-domain loss function and provide new theoretical insights into its effectiveness. Extensive experiments on seven long-term forecasting benchmarks demonstrate that FreDN outperforms state-of-the-art methods by up to 10\%. Furthermore, compared with standard complex-valued architectures, our real-imaginary shared-parameter design reduces the parameter count and computational cost by at least 50\%.

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