mine-frozen-forecaster-s-residual-named-features TEC-0281 · proposedMine the frozen forecaster's residual for named features
Keeps the forecasting backbone frozen and mines its residual with a Monte-Carlo compositional search plus an LLM proposing named feature combinations, binary flags and code, admitting candidates through one source-blind validation gate into
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Keeps the forecasting backbone frozen and mines its residual with a Monte-Carlo compositional search plus an LLM proposing named feature combinations, binary flags and code, admitting candidates through one source-blind validation gate into Filed by the drafting stage from When Do Corrective Features Help? An Agent for Corrective Feature Discovery on Black-Box Forecasters (2608.05207) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Mean reduction in test wMAPE versus the raw frozen backbone across 36 backbone-dataset cells under a rolling-origin, multi-seed protocol, against three external feature-engineering systems (TSFresh, CAAFE, LLM-FE) run through the identical gate and corrector, plus a no-LLM ablation, a covariate-only corrector, and a head-only fine-tune; paired Wilcoxon p<0.01, up to 27% error reduction on the weakest backbones, and the advantage holds at every feature budget K.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
No efficacy claim filed yet. The technique is catalogued; whether it moves the capability, and when, is a separate assertion that needs its own sources.
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Sources
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