เปรียบเทียบวิธี

	แบบจำลอง SARIMA การแตกหักเชิงโครงสร้าง	แบบจำลอง ARIMA (Autoregressive Integrated Moving Average)	แบบจำลอง SARIMA
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model	Regression model
ปีกำเนิด	1970s–1998	1970	1970 (first edition); 1976 (revised)
ผู้ริเริ่ม	Box & Jenkins (SARIMA); Bai & Perron (structural break detection)	George Box and Gwilym Jenkins	Box, Jenkins, and Reinsel
ประเภท	Time series model with regime shifts	Time series forecasting model	Seasonal time series model
แหล่งต้นตำรับ	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.	Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744
ชื่อเรียกอื่น	SARIMA with structural breaks, break-augmented SARIMA, piecewise SARIMA, SARIMA-SB	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	SARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component
ที่เกี่ยวข้อง	3	6	5
สรุป	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.	SARIMA extends ARIMA by adding seasonal autoregressive and moving-average operators to capture repeating patterns at fixed intervals — such as monthly, quarterly, or annual cycles. Denoted SARIMA(p,d,q)(P,D,Q)s, it is the standard workhorse for univariate seasonal time series forecasting in econometrics, economics, and official statistics.