Bandingkan metode

	Model ARMA Bayesian	Model ARIMA (Autoregressive Integrated Moving Average)
Bidang	Ekonometrika	Ekonometrika
Keluarga	Regression model	Regression model
Tahun asal	1970s–1980s	1970
Pencetus	Box & Jenkins (classical ARMA); Bayesian treatment developed through work of Zellner, Geweke, and others in 1970s–1980s	George Box and Gwilym Jenkins
Tipe	Bayesian time series model	Time series forecasting model
Sumber perintis	Geweke, J., & Meese, R. (1981). Estimating regression models of finite but unknown order. International Economic Review, 22(1), 55–70.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Alias	Bayesian ARMA, B-ARMA, Bayesian autoregressive moving average, ARMA with Bayesian inference	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Terkait	6	6
Ringkasan	The Bayesian ARMA model applies Bayesian inference to the classical autoregressive moving average framework for stationary univariate time series. Rather than producing single point estimates for the AR and MA parameters, it yields full posterior distributions, naturally incorporating prior knowledge and providing coherent uncertainty quantification over forecasts and impulse responses.	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.