Sammenlign metoder

	ARMA-modell (Autoregressiv glidende gjennomsnitt)	Autoregressiv modell (AR)	Moving Average (MA)-modell	SARIMA-modell
Fagfelt	Økonometri	Økonometri	Økonometri	Økonometri
Familie	Regression model	Regression model	Regression model	Regression model
Opprinnelsesår	1970	1970s (popularised 1976)	1970	1970 (first edition); 1976 (revised)
Opphavsperson	George E. P. Box and Gwilym M. Jenkins	George E. P. Box and Gwilym M. Jenkins	Box and Jenkins	Box, Jenkins, and Reinsel
Type	Time series model	Time series model	Linear time series model	Seasonal time series model
Opprinnelig kilde	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043	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
Alias	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)	AR model, AR(p) model, autoregression, AR process	MA model, MA(q) process, moving-average process, Box-Jenkins MA	SARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component
Relaterte	5	6	5	5
Sammendrag	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.	An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial series.	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.