Usporedite metode

	Robustni autoregresivni model	ARIMA model (Autoregressive Integrated Moving Average)	Model ARMA (Autoregresivni pokretni prosjek)	Autoregresijski model (AR)
Područje	Ekonometrija	Ekonometrija	Ekonometrija	Ekonometrija
Obitelj	Regression model	Regression model	Regression model	Regression model
Godina nastanka	1986	1970	1970	1970s (popularised 1976)
Tvorac	Martin & Yohai (influential early work); broader robust time series literature	George Box and Gwilym Jenkins	George E. P. Box and Gwilym M. Jenkins	George E. P. Box and Gwilym M. Jenkins
Vrsta	Robust time series model	Time series forecasting model	Time series model	Time series model
Temeljni izvor	Martin, R. D., & Yohai, V. J. (1986). Influence functionals for time series. Annals of Statistics, 14(3), 781–818.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043
Drugi nazivi	robust autoregression, outlier-robust AR, M-estimator AR, heavy-tail AR	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)	AR model, AR(p) model, autoregression, AR process
Srodne	6	6	5	6
Sažetak	The robust AR model fits an autoregressive time series specification using estimation methods — typically M-estimators or bounded-influence estimators — that resist distortion from outliers and heavy-tailed error distributions. Unlike OLS-based AR estimation, robust variants down-weight extreme observations so that a small number of contaminated data points cannot dominate the fitted dynamics.	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.	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.