Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Модел SARIMA× | Модел ARIMA (Авторегресионен интегриран плъзгащ се среден)× | Модел на пълзяща средна (MA)× | |
|---|---|---|---|
| Област | Иконометрия | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 1970 (first edition); 1976 (revised) | 1970 | 1970 |
| Създател≠ | Box, Jenkins, and Reinsel | George Box and Gwilym Jenkins | Box and Jenkins |
| Тип≠ | Seasonal time series model | Time series forecasting model | Linear time series model |
| Основополагащ източник≠ | 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 ↗ | Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744 |
| Други названия | SARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) | MA model, MA(q) process, moving-average process, Box-Jenkins MA |
| Свързани≠ | 5 | 6 | 5 |
| Резюме≠ | 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. | The Moving Average model of order q — written MA(q) — expresses the current value of a time series as a linear combination of the current and past random shocks (innovations). Unlike the AR model which uses lagged values of the series itself, the MA model uses lagged error terms, making it well-suited for capturing short-lived disturbances that dissipate over q periods. |
| ScholarGateНабор от данни ↗ |
|
|
|