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Wasserstein Exponential Smoothing extends forecasting to distributional time series

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method called Wasserstein Exponential Smoothing (WES) to extend exponential smoothing techniques to distributional time series. This approach allows for forecasting when observations are probability distributions rather than single real numbers. The paper details a principled generalization of exponential smoothing within Wasserstein space and demonstrates how to estimate the smoothing parameter by minimizing Wasserstein distance, showing practical effectiveness in financial and energy demand forecasting. AI

IMPACT Introduces a novel statistical method for time series forecasting with distributional data.

RANK_REASON The cluster contains an academic paper detailing a new statistical methodology.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Wasserstein Exponential Smoothing extends forecasting to distributional time series

COVERAGE [2]

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

    Wasserstein Exponential Smoothing

    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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