Methoden vergleichen

	Bayesian SARIMA-Modell	ARIMA-Modell (Autoregressives integriertes gleitendes Durchschnittsmodell)
Fachgebiet	Ökonometrie	Ökonometrie
Familie	Regression model	Regression model
Entstehungsjahr	1970s–1990s	1970
Urheber	Box & Jenkins (classical SARIMA); Bayesian extensions developed through Zellner, Geweke, and later MCMC-era researchers	George Box and Gwilym Jenkins
Typ	Bayesian time-series model	Time series forecasting model
Wegweisende Quelle	Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Aliasnamen	Bayesian SARIMA, Bayesian seasonal ARIMA, BSARIMA, Bayesian seasonal time-series model	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Verwandt	4	6
Zusammenfassung	The Bayesian SARIMA model combines the classical Box-Jenkins Seasonal ARIMA framework with Bayesian inference to handle seasonal time-series data. Rather than producing a single point estimate, it yields a full posterior distribution over model parameters, propagating parameter uncertainty directly into forecasts and enabling principled incorporation of prior knowledge.	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.