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RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting
Retrieval-augmented generation (RAG) complements parametric models with retrieved external evidence. The same idea is attractive for continuous-output regression, but directly reusing retrieved target values is often not robust when samples differ in output level, numerical scale, or local dynamics.
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