Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| SARIMA Model× | ARIMA model× | Autoregressief Model (AR)× | |
|---|---|---|---|
| Vakgebied | Econometrie | Econometrie | Econometrie |
| Familie | Regression model | Regression model | Regression model |
| Jaar van ontstaan≠ | 1970 (first edition); 1976 (revised) | 1970 | 1970s (popularised 1976) |
| Grondlegger≠ | Box, Jenkins, and Reinsel | George Box and Gwilym Jenkins | George E. P. Box and Gwilym M. Jenkins |
| Type≠ | Seasonal time series model | Time series forecasting model | Time series model |
| Oorspronkelijke bron≠ | 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. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043 |
| Aliassen | SARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) | AR model, AR(p) model, autoregression, AR process |
| Verwant≠ | 5 | 6 | 6 |
| Samenvatting≠ | 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. | An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial series. |
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