Bandingkan metode

	Model Rata-rata Bergerak (MA) Robust	Model ARIMA (Autoregressive Integrated Moving Average)
Bidang	Ekonometrika	Ekonometrika
Keluarga	Regression model	Regression model
Tahun asal	1979–2009	1970
Pencetus	Denby & Martin (1979); Muler, Pena & Yohai (2009)	George Box and Gwilym Jenkins
Tipe	Robust time series model	Time series forecasting model
Sumber perintis	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.
Alias	robust MA, robust moving average, M-estimation MA, bounded-influence MA	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Terkait	6	6
Ringkasan	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.