مقایسهٔ روشها
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| مدل ARMA (میانگین متحرک خودرگرسیو)× | مدل آریما (میانگین متحرک یکپارچه خودرگرسیو)× | مدل میانگین متحرک (MA)× | |
|---|---|---|---|
| حوزه | اقتصادسنجی | اقتصادسنجی | اقتصادسنجی |
| خانواده | Regression model | Regression model | Regression model |
| سال پیدایش | 1970 | 1970 | 1970 |
| پدیدآور≠ | George E. P. Box and Gwilym M. Jenkins | George Box and Gwilym Jenkins | Box and Jenkins |
| نوع≠ | Time series model | Time series forecasting model | Linear time series model |
| منبع بنیادین≠ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ | Box, 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 |
| نامهای دیگر | 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 |
| مرتبط≠ | 5 | 6 | 5 |
| خلاصه≠ | 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. |
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