Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель ковзного середнього (MA)× | Модель ARMA (авторегресійна ковзна середня)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи | 1970 | 1970 |
| Автор методу≠ | Box and Jenkins | George E. P. Box and Gwilym M. Jenkins |
| Тип≠ | Linear time series model | Time series model |
| Основоположне джерело≠ | 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. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Інші назви | MA model, MA(q) process, moving-average process, Box-Jenkins MA | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Пов'язані | 5 | 5 |
| Підсумок≠ | 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. | 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. |
| ScholarGateНабір даних ↗ |
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