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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F24%3A00599051" target="_blank" >RIV/67985556:_____/24:00599051 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.tandfonline.com/doi/full/10.1080/03081079.2024.2350542" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/03081079.2024.2350542</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/03081079.2024.2350542" target="_blank" >10.1080/03081079.2024.2350542</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of General Systems

ISSN

0308-1079

e-ISSN

1563-5104

Svazek periodika

53

Číslo periodika v rámci svazku

7-8

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

1146-1160

Kód UT WoS článku

001233832100001

EID výsledku v databázi Scopus

2-s2.0-85194565565