Valid and Expressive Copulas for Irregular Multivariate Time Series
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866916039585955840 |
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| author | Klötergens, Christian Hanika, Tom Schmidt-Thieme, Lars Yalavarthi, Vijaya Krishna |
| author_facet | Klötergens, Christian Hanika, Tom Schmidt-Thieme, Lars Yalavarthi, Vijaya Krishna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_23632 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Valid and Expressive Copulas for Irregular Multivariate Time Series Klötergens, Christian Hanika, Tom Schmidt-Thieme, Lars Yalavarthi, Vijaya Krishna Machine Learning 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. |
| title | Valid and Expressive Copulas for Irregular Multivariate Time Series |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.23632 |