| bibtype |
J -
Journal Article
|
| ARLID |
0599051 |
| utime |
20241209114618.6 |
| mtime |
20241007235959.9 |
| SCOPUS |
85194565565 |
| WOS |
001233832100001 |
| DOI |
10.1080/03081079.2024.2350542 |
| title
(primary) (eng) |
Improving the accuracy of predictions in multivariate time series using dynamic vine copulas |
| specification |
| page_count |
15 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0256794 |
| ISSN |
0308-1079 |
| title
|
International Journal of General Systems |
| volume_id |
53 |
| page_num |
1146-1160 |
| publisher |
|
|
| keyword |
vine copula |
| keyword |
dynamic copula |
| keyword |
time series |
| keyword |
change point |
| author
(primary) |
| ARLID |
cav_un_auth*0436913 |
| name1 |
Sheikhi |
| name2 |
A. |
| country |
IR |
| share |
40 |
| garant |
K |
|
| author
|
| ARLID |
cav_un_auth*0473809 |
| name1 |
Dalla Valle |
| name2 |
L. |
| country |
GB |
| share |
30 |
|
| author
|
| ARLID |
cav_un_auth*0101163 |
| name1 |
Mesiar |
| name2 |
Radko |
| institution |
UTIA-B |
| full_dept (cz) |
Ekonometrie |
| full_dept |
Department of Econometrics |
| department (cz) |
E |
| department |
E |
| full_dept |
Department of Econometrics |
| share |
30 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| abstract
(eng) |
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. |
| result_subspec |
WOS |
| RIV |
BA |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2025 |
| num_of_auth |
3 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0357454 |
| confidential |
S |
| mrcbC91 |
C |
| mrcbT16-e |
COMPUTERSCIENCETHEORYMETHODS |
| mrcbT16-j |
0.507 |
| mrcbT16-s |
0.528 |
| mrcbT16-D |
Q3 |
| mrcbT16-E |
Q3 |
| arlyear |
2024 |
| mrcbU14 |
85194565565 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001233832100001 WOS |
| mrcbU63 |
cav_un_epca*0256794 International Journal of General Systems 53 7-8 2024 1146 1160 0308-1079 1563-5104 Taylor & Francis |
|