השוואת שיטות

	מודל ARIMA (Autoregressive Integrated Moving Average)	מודל ממוצע נע (MA)
תחום	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model
שנת המקור	1970	1970
הוגה השיטה	George Box and Gwilym Jenkins	Box and Jenkins
סוג	Time series forecasting model	Linear time series model
מקור מכונן	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
כינויים	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	MA model, MA(q) process, moving-average process, Box-Jenkins MA
קשורות	6	5
תקציר	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.