0599051 20241209114618.620241007235959.9 85194565565 001233832100001 10.1080/03081079.2024.2350542 15 s. P cav_un_epca*02567940308-1079531146-1160Taylor & Francis vine copula dynamic copula time series change point cav_un_auth*0436913 Sheikhi A. IR 40 K cav_un_auth*0473809 Dalla Valle L. GB 30 cav_un_auth*0101163 Mesiar Radko UTIA-B Ekonometrie Department of Econometrics E E Department of Econometrics 30 Ústav teorie informace a automatizace AV ČR, v. v. i. https://library.utia.cas.cz/separaty/2024/E/mesiar-0599051.pdf https://www.tandfonline.com/doi/full/10.1080/03081079.2024.2350542 In this work, we deal with non-stationary multivariate time series, proposing a method which uses copulas to produce more accurate forecasting. The idea is to apply a copula-based approach to identify change points and then split the time series into consecutive segments based on these change points. In each segment, we define the best-fitting copula family and forecast values of the time series of each segment using the corresponding fitting copula. We apply our model to a financial data set to test the predictive power of our approach. A simulation study is also presented for a detailed illustration and assessment of our proposed methodology. Based on the results of numerical analysis, we observed that our proposed approach will help us to improve the accuracy of forecasting in comparison with other existing methods such as traditional time series forecasting as well as neural network forecasting. WOS BA 10000 10100 10103 2025 3 RVO:67985556 https://hdl.handle.net/11104/0357454 S C COMPUTERSCIENCE.THEORY&METHODS 2.2 0.7 11.5 0.00089 0.527 1508 56 0.556 47.16 2.95 320 Q2 2.200 62 Q2 70.4 0.75 Q2 70.4 2024 85194565565 SCOPUS PUBMED 001233832100001 WOS cav_un_epca*0256794 International Journal of General Systems 53 7-8 2024 1146 1160 0308-1079 1563-5104 Taylor & Francis