เปรียบเทียบวิธี

	การถดถอยกำลังสองน้อยที่สุดสามัญ (OLS)	Seasonal ARIMA (SARIMA)
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model
ปีกำเนิด	2019	2015
ผู้ริเริ่ม	Wooldridge (textbook treatment); classical least squares	Box & Jenkins (seasonal extension of ARIMA)
ประเภท	Linear regression	Seasonal time-series model
แหล่งต้นตำรับ	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Box, G.E.P., Jenkins, G.M., Reinsel, G.C. & Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021
ชื่อเรียกอื่น	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA
ที่เกี่ยวข้อง	5	5
สรุป	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period.