SMLDR: Spectral Memory Learner with Dual-Retrieval for Time Series Forecasting
This paper re-examines frequency-domain modeling from the perspective of spectral non-identifiability and proposes a Spectral Memory Learner with Dual Retrieval (SMLDR) for time series forecasting, which represents periodic components as reusable spectral prototypes stored in learnable memory.