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Модель ковзного середнього (MA)×Модель ARIMA (Авторегресійна інтегрована ковзна середня)×Модель SARIMA×
ГалузьЕконометрикаЕконометрикаЕконометрика
РодинаRegression modelRegression modelRegression model
Рік появи197019701970 (first edition); 1976 (revised)
Автор методуBox and JenkinsGeorge Box and Gwilym JenkinsBox, Jenkins, and Reinsel
ТипLinear time series modelTime series forecasting modelSeasonal time series model
Основоположне джерелоBox, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744
Інші назвиMA model, MA(q) process, moving-average process, Box-Jenkins MAARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)SARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component
Пов'язані565
ПідсумокThe 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.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.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.
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ScholarGateПорівняння методів: Moving Average Model · ARIMA model · SARIMA model. Отримано 2026-06-18 з https://scholargate.app/uk/compare