Salīdzināt metodes

	SARIMA modelis ar strukturālām pārtraukumiem	ARIMA modelis (autoregresīvais integrētais slīdošais vidējais)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1970s–1998	1970
Autors	Box & Jenkins (SARIMA); Bai & Perron (structural break detection)	George Box and Gwilym Jenkins
Tips	Time series model with regime shifts	Time series forecasting model
Pirmavots	Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Citi nosaukumi	SARIMA with structural breaks, break-augmented SARIMA, piecewise SARIMA, SARIMA-SB	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Saistītās	3	6
Kopsavilkums	The Structural Break SARIMA model extends the classical Seasonal ARIMA framework by explicitly detecting and accommodating abrupt, permanent shifts in the level, trend, or seasonal pattern of a time series. Rather than forcing a single SARIMA specification across the entire sample, the model partitions the series at estimated breakpoints and fits separate SARIMA processes to each resulting segment, producing more accurate forecasts and reliable inference in the presence of regime changes.	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.