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New ScaleIn method improves time series foundation models

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的时间序列基础模型（TSFM）训练方法ScaleIn，旨在解决数据集中显著尺度变化带来的问题。现有的Reversible Instance Normalization（ReVIN）等方法可能会无意中根据尺度对序列进行加权，导致ScaleCon，即大尺度序列主导训练。然而，ScaleIn确保了优化轨迹对训练序列的任意重缩放都保持不变，无论使用何种损失函数，只要它是p次齐次的。这种方法已展示出显著的改进，在GIFT-Eval上平均将平均绝对尺度误差（MASE）降低了18.8%，在M-competitions上降低了21.9%，涵盖了各种TSFM架构和基准测试。 AI

影响 这种新的训练方法ScaleIn显著提高了时间序列基础模型的性能，有可能在各个领域带来更准确的预测和分析。

排序理由 该集群包含一篇学术论文，详细介绍了训练时间序列基础模型的新方法。 [lever_c_demoted from research: ic=1 ai=1.0]

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New ScaleIn method improves time series foundation models

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该集群包含一篇学术论文，详细介绍了训练时间序列基础模型的新方法。 [lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ignacy Stepka, Willa Potosnak, Kin G. Olivares, Artur Dubrawski ·

    Scale-Invariant Training for Time Series Foundation Models

    arXiv:2610.07324v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) are trained on large collections of time series datasets that span various morphologies and domains. This setting exposes models to series whose scales -- typical magnitudes of their values --…

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