Usporedite metode

	Model ARMA (Autoregresivni pokretni prosjek)	ARIMA model (Autoregressive Integrated Moving Average)	Model pomičnih prosjeka (MA)	SARIMA model
Područje	Ekonometrija	Ekonometrija	Ekonometrija	Ekonometrija
Obitelj	Regression model	Regression model	Regression model	Regression model
Godina nastanka	1970	1970	1970	1970 (first edition); 1976 (revised)
Tvorac	George E. P. Box and Gwilym M. Jenkins	George Box and Gwilym Jenkins	Box and Jenkins	Box, Jenkins, and Reinsel
Vrsta	Time series model	Time series forecasting model	Linear time series model	Seasonal time series model
Temeljni izvor	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	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
Drugi nazivi	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	MA model, MA(q) process, moving-average process, Box-Jenkins MA	SARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component
Srodne	5	6	5	5
Sažetak	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 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.	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.