Salīdzināt metodes

	ARMA modelis (Autoregresīvs vidējais aritmētiskais)	Modelis ar slīdošo vidējo (MA)	SARIMA modelis
Nozare	Ekonometrija	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	1970	1970	1970 (first edition); 1976 (revised)
Autors	George E. P. Box and Gwilym M. Jenkins	Box and Jenkins	Box, Jenkins, and Reinsel
Tips	Time series model	Linear time series model	Seasonal time series model
Pirmavots	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	Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744
Citi nosaukumi	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)	MA model, MA(q) process, moving-average process, Box-Jenkins MA	SARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component
Saistītās	5	5	5
Kopsavilkums	The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting.	The Moving Average model of order q — written MA(q) — expresses the current value of a time series as a linear combination of the current and past random shocks (innovations). Unlike the AR model which uses lagged values of the series itself, the MA model uses lagged error terms, making it well-suited for capturing short-lived disturbances that dissipate over q periods.	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.