Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robustais ARIMA modelis× | ARIMA modelis (autoregresīvais integrētais slīdošais vidējais)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1986–1993 | 1970 |
| Autors≠ | Tsay (1986); Chen & Liu (1993) | George Box and Gwilym Jenkins |
| Tips≠ | Robust time series model | Time series forecasting model |
| Pirmavots≠ | Tsay, R. S. (1986). Time series model specification in the presence of outliers. Journal of the American Statistical Association, 81(393), 132–141. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Citi nosaukumi | robust ARIMA, outlier-resistant ARIMA, robust time series estimation, ARIMA with outlier detection | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Saistītās≠ | 4 | 6 |
| Kopsavilkums≠ | Robust ARIMA extends the classical ARIMA framework to detect and correct the influence of outliers and structural breaks during estimation. By jointly identifying anomalous observations and re-estimating model parameters, it produces coefficient estimates and forecasts that are far less distorted by isolated shocks or data errors than standard ARIMA. | 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. |
| ScholarGateDatu kopa ↗ |
|
|