Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель ARIMA (Авторегресійна інтегрована ковзна середня)× | Модель ковзного середнього (MA)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи | 1970 | 1970 |
| Автор методу≠ | George Box and Gwilym Jenkins | Box and Jenkins |
| Тип≠ | Time series forecasting model | Linear time series model |
| Основоположне джерело≠ | 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 |
| Інші назви | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) | MA model, MA(q) process, moving-average process, Box-Jenkins MA |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | 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. |
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