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New MELO method hedges memory horizons for non-stationary prediction

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了MELO，一种新颖的模型无关的在线预测方法，可以对不同适应尺度进行对冲。MELO使用指数加权最小二乘适应专家包装基础预测器，并使用无参数在线聚合规则聚合它们的预测。在COVID-19封锁期间对法国电力负荷预测的评估中，与基线方法和使用外部策略协变量的参考模型相比，MELO显著降低了RMSE。 AI

影响 引入了一种处理预测任务中非平稳数据的新方法，有可能提高动态环境中的预测准确性。

排序理由 这是一篇详细介绍新预测方法的学术论文。

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

New MELO method hedges memory horizons for non-stationary prediction

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Yutong Wang, Yannig Goude, Qiwei Yao ·

    通过在线聚合对非平稳预测的记忆视界进行对冲

    arXiv:2605.06541v1 Announce Type: new Abstract: We study online prediction under distribution shift, where inputs arrive chronologically and outcomes are revealed only after prediction. In this setting, predictors must remain stable in quiet regimes yet adapt when regimes shift, …

  2. arXiv stat.ML TIER_1 English(EN) · Qiwei Yao ·

    通过在线聚合对非平稳预测进行对冲记忆视界

    We study online prediction under distribution shift, where inputs arrive chronologically and outcomes are revealed only after prediction. In this setting, predictors must remain stable in quiet regimes yet adapt when regimes shift, and the right adaptation memory is unknown in ad…

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