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English(EN) Valid and Expressive Copulas for Irregular Multivariate Time Series

新的 Copula 模型推动不规则时间序列预测发展

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了 CopFITi，这是一种新颖的 copula 模型，专为不规则多元时间序列的概率预测而设计。该模型将单个时间序列的归一化流与高斯混合 copula 相结合，以捕捉联合依赖关系。实验表明，CopFITi 通过将边际与联合结构分离，优于现有方法，为该数据类型的密度建模树立了新的最先进水平。 AI

影响 为不规则多元时间序列的密度建模树立了新的最先进水平，有可能提高复杂数据场景下的预测准确性。

排序理由 该集群包含一篇详细介绍新模型及其实验结果的学术论文。

在 arXiv cs.LG 阅读 →

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

新的 Copula 模型推动不规则时间序列预测发展

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Christian Kl\"otergens, Tom Hanika, Lars Schmidt-Thieme, Vijaya Krishna Yalavarthi ·

    用于不规则多元时间序列的有效且富有表现力的Copula

    arXiv:2605.23632v1 Announce Type: new Abstract: We introduce CopFITi, a copula model for probabilistic forecasting of irregular multivariate time series (IMTS). Our model combines the expressivity of normalizing flows for univariate marginals with the consistency and flexibility …

  2. arXiv cs.LG TIER_1 English(EN) · Vijaya Krishna Yalavarthi ·

    用于不规则多元时间序列的有效且富有表现力的Copula

    We introduce CopFITi, a copula model for probabilistic forecasting of irregular multivariate time series (IMTS). Our model combines the expressivity of normalizing flows for univariate marginals with the consistency and flexibility of a Gaussian Mixture Copula for the joint depen…

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