Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Modelu Imara ya ARMA× | Modeli ya ARMA (Autoregressive Moving Average)× | |
|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1986 | 1970 |
| Mwanzilishi≠ | Martin & Yohai (1986); broader robust time series literature | George E. P. Box and Gwilym M. Jenkins |
| Aina≠ | Robust time series model | Time series model |
| Chanzo asilia≠ | Franses, P. H., & Ghijsels, H. (1999). Additive outliers, GARCH and forecasting volatility. International Journal of Forecasting, 15(1), 1-9. link ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Majina mbadala | robust ARMA, outlier-robust ARMA, M-estimator ARMA, resistant ARMA estimation | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | The Robust ARMA model extends the classical Autoregressive Moving Average framework by replacing the sensitive least-squares loss with outlier-resistant estimation methods — typically M-estimators or median-based approaches. This protects coefficient estimates and forecasts from being distorted by additive outliers, level shifts, or innovational outliers that are common in economic and financial time series. | 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. |
| ScholarGateSeti ya data ↗ |
|
|