Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Modelis ar slīdošo vidējo (MA)× | ARIMA modelis (autoregresīvais integrētais slīdošais vidējais)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads | 1970 | 1970 |
| Autors≠ | Box and Jenkins | George Box and Gwilym Jenkins |
| Tips≠ | Linear time series model | Time series forecasting model |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | MA model, MA(q) process, moving-average process, Box-Jenkins MA | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | 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. | 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. |
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