Forecasting and change point test for nonlinear heteroscedastic time series based on support vector regression.
PLoS One
; 17(12): e0278816, 2022.
Article
em En
| MEDLINE
| ID: mdl-36584161
ABSTRACT
SVR-ARMA-GARCH models provide flexible model fitting and good predictive powers for nonlinear heteroscedastic time series datasets. In this study, we explore the change point detection problem in the SVR-ARMA-GARCH model using the residual-based CUSUM test. For this task, we propose an alternating recursive estimation (ARE) method to improve the estimation accuracy of residuals. Moreover, we suggest using a new testing method with a time-varying control limit that significantly improves the detection power of the CUSUM test. Our numerical analysis exhibits the merits of the proposed methods in SVR-ARMA-GARCH models. A real data example is also conducted using BDI data for illustration, which also confirms the validity of our methods.
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Base de dados:
MEDLINE
Assunto principal:
Fatores de Tempo
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article