Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель SARIMA× | Модель ARIMA (авторегрессионная интегрированная скользящая средняя)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1970 (first edition); 1976 (revised) | 1970 |
| Автор метода≠ | Box, Jenkins, and Reinsel | George Box and Gwilym Jenkins |
| Тип≠ | Seasonal time series model | Time series forecasting model |
| Основополагающий источник≠ | Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744 | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Другие названия | SARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Связанные≠ | 5 | 6 |
| Сводка≠ | SARIMA extends ARIMA by adding seasonal autoregressive and moving-average operators to capture repeating patterns at fixed intervals — such as monthly, quarterly, or annual cycles. Denoted SARIMA(p,d,q)(P,D,Q)s, it is the standard workhorse for univariate seasonal time series forecasting in econometrics, economics, and official statistics. | 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 ↗ |
|
|