Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| SARIMA-modellen× | ARMA-modell (Autoregressiv glidande medelvärde)× | Moving Average (MA) Model× | |
|---|---|---|---|
| Ämnesområde | Ekonometri | Ekonometri | Ekonometri |
| Familj | Regression model | Regression model | Regression model |
| Ursprungsår≠ | 1970 (first edition); 1976 (revised) | 1970 | 1970 |
| Upphovsperson≠ | Box, Jenkins, and Reinsel | George E. P. Box and Gwilym M. Jenkins | Box and Jenkins |
| Typ≠ | Seasonal time series model | Time series model | Linear time series model |
| Ursprungskälla≠ | 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., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744 |
| Alias | SARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) | MA model, MA(q) process, moving-average process, Box-Jenkins MA |
| Närliggande | 5 | 5 | 5 |
| Sammanfattning≠ | 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 ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting. | 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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