Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Нелинейная модель ARMA (NARMA)× | Модель ARMA (авторегрессионная скользящая средняя)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1980s–1990s | 1970 |
| Автор метода≠ | Tong (1990); Granger & Terasvirta (1993) | George E. P. Box and Gwilym M. Jenkins |
| Тип≠ | Nonlinear time series model | Time series model |
| Основополагающий источник≠ | Tong, H. (1990). Non-linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 978-0198522300 | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Другие названия | NARMA, nonlinear ARMA, NLARMA, nonlinear autoregressive moving average | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Связанные≠ | 2 | 5 |
| Сводка≠ | The Nonlinear ARMA (NARMA) model extends the classical linear ARMA framework by allowing the conditional mean to depend on past observations and past errors through an arbitrary nonlinear function. It captures complex dynamics — such as regime changes, asymmetric cycles, and threshold effects — that linear models miss, making it valuable for economic and financial time series. | 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. |
| ScholarGateНабор данных ↗ |
|
|