{"ID":2841890,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.11817","arxiv_id":"2511.11817","title":"FreDN: Spectral Disentanglement for Time Series Forecasting via Learnable Frequency Decomposition","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\\%.","short_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 t...","url_abs":"https://arxiv.org/abs/2511.11817","url_pdf":"https://arxiv.org/pdf/2511.11817v2","authors":"[\"Zhongde An\",\"Jinhong You\",\"Jiyanglin Li\",\"Yiming Tang\",\"Wen Li\",\"Heming Du\",\"Shouguo Du\"]","published":"2025-11-14T19:13:24Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\"]","methods":"[]","has_code":false}