Salīdzināt metodes

	Nelineārs SARIMA modelis	ARIMA modelis (autoregresīvais integrētais slīdošais vidējais)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1990–2000	1970
Autors	Tong (1990) for threshold nonlinear extensions; Franses & van Dijk (2000) for empirical finance applications	George Box and Gwilym Jenkins
Tips	Nonlinear time series model	Time series forecasting model
Pirmavots	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.
Citi nosaukumi	NL-SARIMA, nonlinear seasonal ARIMA, threshold SARIMA, smooth transition SARIMA	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Saistītās	3	6
Kopsavilkums	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.