Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель ARMA (авторегрессионная скользящая средняя)× | Модель скользящего среднего (MA)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления | 1970 | 1970 |
| Автор метода≠ | George E. P. Box and Gwilym M. Jenkins | Box and Jenkins |
| Тип≠ | Time series model | Linear time series model |
| Основополагающий источник≠ | 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 |
| Другие названия | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) | MA model, MA(q) process, moving-average process, Box-Jenkins MA |
| Связанные | 5 | 5 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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