ECHO: Correcting Time Series Foundation Model Forecasts with Historical Analogues and Errors
This work proposes ECHO, a black-box forecast correction framework that uses AnalogE to assess retrieved historical analogues together with the current context and base forecast, learning transferable corrections through cross-domain pretraining and dynamically combines the two corrections according to their observed f...
Bo-Ya Zhang, Xilin Dai, Yi-Lin Wang et al.
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