Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель SARIMA× | Модель ковзного середнього (MA)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1970 (first edition); 1976 (revised) | 1970 |
| Автор методу≠ | Box, Jenkins, and Reinsel | Box and Jenkins |
| Тип≠ | Seasonal time series model | Linear time series model |
| Основоположне джерело | 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., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744 |
| Інші назви | SARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component | MA model, MA(q) process, moving-average process, Box-Jenkins MA |
| Пов'язані | 5 | 5 |
| Підсумок≠ | 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 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. |
| ScholarGateНабір даних ↗ |
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