Vertaile menetelmiä

	Robustti autoregressiivinen malli	ARIMA-malli (Autoregressiivinen integroitu liukuva keskiarvo)
Tieteenala	Ekonometria	Ekonometria
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	1986	1970
Kehittäjä	Martin & Yohai (influential early work); broader robust time series literature	George Box and Gwilym Jenkins
Tyyppi	Robust time series model	Time series forecasting model
Alkuperäislähde	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.
Rinnakkaisnimet	robust autoregression, outlier-robust AR, M-estimator AR, heavy-tail AR	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Liittyvät	6	6
Tiivistelmä	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.