RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting
Overall, RATL shifts the retrieved object from historical target values to base-model-specific historical forecast errors, providing a plug-in, residual-memory-based paradigm for learned feedback correction in continuous-output forecasting.
Yu-Chen He, Yueyang Cang, Zhi-Yuan Ning et al.
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