English(EN) A Later Test Set Is Not a New Domain: Pretraining Familiarity Survives a Contamination-Free Hold-Out

时间序列AI模型因预训练的熟悉度而表现出色，而非预测技能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-09 15:52

一项新研究表明，预训练的熟悉度而非真正的预测能力，显著影响了时间序列基础模型的性能。研究人员创建了一个测试集，其中包含模型发布日期之后发布的数据，以减轻预训练语料库的污染。结果显示，预训练模型普遍优于其他模型，但在每日汇率方面，它们的优势显著减弱，与简单方法无异。该研究得出结论，基准测试需要相对于已披露语料库的领域留出，并且从业者应考虑模型是否在其特定领域进行了训练。 AI

影响强调了对时间序列模型进行更鲁棒评估的必要性，影响了对其能力进行评估和理解的方式。

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-09-09 15:52

稍后的测试集并非新领域：预训练的熟悉度在无污染的留出集中得以保留

Time-series foundation models are evaluated almost exclusively on public archives that predate them, so a strong score cannot be separated from having seen the test set during pretraining. The obvious remedy is a hold-out that postdates the models. We build one: thirteen forecast…

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稍后的测试集并非新领域：预训练的熟悉度在无污染的留出集中得以保留

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