Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель ARMA (авторегрессионная скользящая средняя)× | Авторегрессионная модель (AR)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1970 | 1970s (popularised 1976) |
| Автор метода | George E. P. Box and Gwilym M. Jenkins | George E. P. Box and Gwilym M. Jenkins |
| Тип | Time series model | 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. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043 |
| Другие названия | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) | AR model, AR(p) model, autoregression, AR process |
| Связанные≠ | 5 | 6 |
| 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. | An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial series. |
| ScholarGateНабор данных ↗ |
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