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. Moreover, conventional forecasting pipelines generally use residuals for model optimization and…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
