Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| SARIMA modelis× | Modelis ar slīdošo vidējo (MA)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1970 (first edition); 1976 (revised) | 1970 |
| Autors≠ | Box, Jenkins, and Reinsel | Box and Jenkins |
| Tips≠ | Seasonal time series model | Linear time series 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., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744 |
| Citi nosaukumi | SARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component | MA model, MA(q) process, moving-average process, Box-Jenkins MA |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. |
| ScholarGateDatu kopa ↗ |
|
|