Usporedite metode

	Nelinearni SARIMA model	ARIMA model (Autoregressive Integrated Moving Average)	Model GARCH (Prognoziranje volatilnosti)
Područje	Ekonometrija	Ekonometrija	Ekonometrija
Obitelj	Regression model	Regression model	Regression model
Godina nastanka	1990–2000	1970	1986
Tvorac	Tong (1990) for threshold nonlinear extensions; Franses & van Dijk (2000) for empirical finance applications	George Box and Gwilym Jenkins	Tim Bollerslev
Vrsta	Nonlinear time series model	Time series forecasting model	Conditional volatility model
Temeljni izvor	Tong, H. (1990). Non-linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 978-0198523000	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Drugi nazivi	NL-SARIMA, nonlinear seasonal ARIMA, threshold SARIMA, smooth transition SARIMA	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Srodne	3	6	5
Sažetak	The Nonlinear SARIMA model extends the classical Seasonal ARIMA framework by replacing the linear conditional mean function with a nonlinear specification — such as threshold switching or smooth transition — while retaining seasonal differencing and lag structure. It is used when seasonal time series exhibit regime-dependent dynamics, asymmetric adjustment, or other nonlinear patterns that a linear model cannot capture.	The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.	The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.