Sammenlign metoder

	Robust Moving Average (MA) Model	ARIMA-modellen (Autoregressive Integrated Moving Average)
Fagområde	Økonometri	Økonometri
Familie	Regression model	Regression model
Oprindelsesår	1979–2009	1970
Ophavsperson	Denby & Martin (1979); Muler, Pena & Yohai (2009)	George Box and Gwilym Jenkins
Type	Robust time series model	Time series forecasting model
Oprindelig kilde	Denby, L., & Martin, R. D. (1979). Robust estimation of the first-order autoregressive parameter. Journal of the American Statistical Association, 74(365), 140–146.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Aliasser	robust MA, robust moving average, M-estimation MA, bounded-influence MA	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Relaterede	6	6
Resumé	The Robust MA model applies robust estimation — typically M-estimation or bounded-influence methods — to the Moving Average time series model. By replacing the ordinary least squares loss with a bounded loss function, it produces parameter estimates that are far less sensitive to outliers, additive noise spikes, or heavy-tailed error distributions than the classical Gaussian MA.	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.