Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастная модель SARIMA× | Модель ARIMA (авторегрессионная интегрированная скользящая средняя)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1979–2009 | 1970 |
| Автор метода≠ | Muler, Peña & Yohai (robust ARMA); earlier foundation by Denby & Martin (1979) | George Box and Gwilym Jenkins |
| Тип≠ | Robust time-series model | Time series forecasting model |
| Основополагающий источник≠ | Muler, N., Peña, D., & Yohai, V. J. (2009). Robust estimation for ARMA models. The Annals of Statistics, 37(2), 816–840. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Другие названия | robust SARIMA, outlier-resistant SARIMA, robust seasonal ARIMA, M-estimator SARIMA | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Связанные≠ | 4 | 6 |
| Сводка≠ | Robust SARIMA extends the classical Seasonal ARIMA framework by replacing the standard least-squares criterion with a robust loss function — such as an M-estimator — so that outliers and heavy-tailed innovations in seasonal time series cannot distort parameter estimates or invalidate forecasts. | 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 ↗ |
|
|