2026
FreDN: Spectral Disentanglement for Time Series Forecasting via Learnable Frequency Decomposition
AAAI 2026technical
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 spectral entanglem