feed-integer-rounded-values-normalized-ones-llm-forecasters TEC-0031 · proposedFeed integer-rounded values, not normalized ones, to LLM for
Round each numeric time-series value to the nearest integer before serializing it into the prompt, preserving the original scale instead of standardizing or instance-normalizing.
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.
Round each numeric time-series value to the nearest integer before serializing it into the prompt, preserving the original scale instead of standardizing or instance-normalizing. Filed by the drafting stage from PRICE: A Systematic Study of LLM Adaptation Choices for Bitcoin Price Forecasting (2609.05235) 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: Validation-set MAPE/SMAPE/NRMSE on BTC/USDT hourly forecasting under a fixed zero-shot prompt with only the representation varied: rounded 3.01/3.05/0.05 vs raw 4.23/5.42/0.12, standard normalization 9.43/9.39/0.16, instance normalization 548.36/132.19/1.41; valid-output count unchanged at 1441.. 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.
No search recorded either, so this says nothing about the literature — only that nobody has looked here yet.
Code
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Sources
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