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| Model ARMA (Autoregresyjny Model Średniej Ruchomej)× | Model średniej ruchomej (MA)× | |
|---|---|---|
| Dziedzina | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania | 1970 | 1970 |
| Twórca≠ | George E. P. Box and Gwilym M. Jenkins | Box and Jenkins |
| Typ≠ | Time series model | Linear time series model |
| Źródło pierwotne≠ | 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 |
| Inne nazwy | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) | MA model, MA(q) process, moving-average process, Box-Jenkins MA |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | 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 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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