提出首个自洽的不规则多变量时间序列概率预测模型
Valid and Expressive Copulas for Irregular Multivariate Time Series
- 用归一化流建模边缘分布，混合高斯耦合建模联合依赖
- 在联合密度建模上达到新基准，优于直接拟合全联合的架构
- 适合需要精准边缘与联合建模的金融、医疗时序预测场景
我们提出 CopFITi，一种用于不规则多变量时间序列（IMTS）概率预测的耦合模型。该模型结合了归一化流在单变量边缘上的表达能力与混合高斯耦合在联合依赖结构上的一致性和灵活性。实验表明，将边缘与联合分离建模的耦合方法，相比直接拟合完整联合分布的架构，能获得更优的边缘模型性能。CopFITi 是首个通过构造保证边缘一致性的一致性耦合模型，并在联合 IMTS 密度建模上建立了新基准。
原文摘要 · Abstract (English)
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 dependency structure. Our experiments show that copula-based approaches, which decouple the marginals from the joint, yield better marginal models than architectures that directly fit the full joint. With CopFITi, we propose the first IMTS copula that is marginalization-consistent by construction and establish a new state of the art in joint IMTS density modeling.
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