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Wasserstein Exponential Smoothing 将预测扩展到分布时间序列

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为 Wasserstein Exponential Smoothing (WES) 的新方法，将指数平滑技术扩展到分布时间序列。这种方法允许在观测值是概率分布而非单个实数时进行预测。该论文详细介绍了在 Wasserstein 空间内对指数平滑的原则性推广，并展示了如何通过最小化 Wasserstein 距离来估计平滑参数，在金融和能源需求预测中显示出实际效果。 AI

影响 为具有分布数据的时序预测引入了一种新颖的统计方法。

排序理由 该集群包含一篇详细介绍新统计方法的学术论文。

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Wasserstein Exponential Smoothing 将预测扩展到分布时间序列

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Takuo Matsubara, Peiwen Jiang, Minh-Ngoc Tran, Wilson Ye Chen ·

    Wasserstein 指数平滑

    arXiv:2606.05560v1 Announce Type: cross Abstract: Exponential smoothing (ES) often outperforms other techniques in time series forecasting across a wide range of data-generating processes. While ES has traditionally been applied to time series in $\mathbb{R}$, this paper extends …

  2. arXiv stat.ML TIER_1 English(EN) · Wilson Ye Chen ·

    Wasserstein Exponential Smoothing

    Exponential smoothing (ES) often outperforms other techniques in time series forecasting across a wide range of data-generating processes. While ES has traditionally been applied to time series in $\mathbb{R}$, this paper extends the methodology to distributional time series, whe…

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