Improving the accuracy of predictions in multivariate time series using dynamic vine copulas-Reference-Cited by-同舟云学术

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Improving the accuracy of predictions in multivariate time series using dynamic vine copulas

Published:2024-05-28 Issue: Volume: Page:1-15
ISSN:0308-1079
Container-title:International Journal of General Systems
language:en
Short-container-title:International Journal of General Systems

Author:

Sheikhi Ayyub¹,Dalla Valle Luciana²,Mesiar Radko³⁴

Affiliation:

1. Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran

2. School of Engineering, Computing and Mathematics, Plymouth University, Plymouth, UK

3. Department of Mathematics and Descriptive Geometry, Faculty of Civil Engineering, STU Bratislava, Slovakia

4. UTIA AV ČR, Prague 8, Czech Republic

Funder

EPSRC

Slovak Academic Information Agency

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/03081079.2024.2350542

Reference56 articles.

1. Pair-copula constructions of multiple dependence

2. Flexible dynamic vine copula models for multivariate time series data

3. Ansell Lauren and Luciana Dalla Valle. 2021. “Social Media Integration of Flood Data: A Vine Copula-Based Approach.” arXiv:2104.01869.

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1. A reliable ensemble forecasting modeling approach for complex time series with distributionally robust optimization;Computers & Operations Research;2025-01

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