A Generalization of the Arima Model to the Nonlinear and Continuous Cases.

Autor:	Kushnir, M., Tokarieva, K.
Předmět:	STOCHASTIC differential equations FUNCTIONAL differential equations BOX-Jenkins forecasting GENERALIZATION RANDOM variables GARCH model
Zdroj:	Cybernetics & Systems Analysis; Nov2023, Vol. 59 Issue 6, p900-909, 10p
Abstrakt:	A method of extending the classical ARIMA and GARCH models to the continuous and nonlinear cases is considered. Stochastic functional differential equations, which are a natural generalization of the sums of independent random variables, are considered extended models. Along with the new model, a relevant optimization problem of estimating model parameters is considered, and the non-parametric problem is reduced to a parametric one. The new model was tested on real data, and the forecast results were compared with classical models. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu Full text from SpringerLink