Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастная модель скользящего среднего (MA)× | Модель ARIMA (авторегрессионная интегрированная скользящая средняя)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1979–2009 | 1970 |
| Автор метода≠ | Denby & Martin (1979); Muler, Pena & Yohai (2009) | George Box and Gwilym Jenkins |
| Тип≠ | Robust time series model | Time series forecasting model |
| Основополагающий источник≠ | Denby, L., & Martin, R. D. (1979). Robust estimation of the first-order autoregressive parameter. Journal of the American Statistical Association, 74(365), 140–146. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Другие названия | robust MA, robust moving average, M-estimation MA, bounded-influence MA | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Связанные | 6 | 6 |
| Сводка≠ | The Robust MA model applies robust estimation — typically M-estimation or bounded-influence methods — to the Moving Average time series model. By replacing the ordinary least squares loss with a bounded loss function, it produces parameter estimates that are far less sensitive to outliers, additive noise spikes, or heavy-tailed error distributions than the classical Gaussian MA. | The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics. |
| ScholarGateНабор данных ↗ |
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