Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| ARIMA-modell (Autoregressiv Integrerad Glidande Medelvärdesmodell)× | ARMA-modell (Autoregressiv glidande medelvärde)× | |
|---|---|---|
| Ämnesområde | Ekonometri | Ekonometri |
| Familj | Regression model | Regression model |
| Ursprungsår | 1970 | 1970 |
| Upphovsperson≠ | George Box and Gwilym Jenkins | George E. P. Box and Gwilym M. Jenkins |
| Typ≠ | Time series forecasting model | Time series model |
| Ursprungskälla | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Alias | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Närliggande≠ | 6 | 5 |
| Sammanfattning≠ | 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 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. |
| ScholarGateDatamängd ↗ |
|
|