Salīdzināt metodes

	Bayesiešu ARIMA modelis	ARIMA modelis (autoregresīvais integrētais slīdošais vidējais)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1970s (ARIMA); Bayesian extension prominent from 1990s	1970
Autors	Pole, West & Harrison (Bayesian treatment); Box & Jenkins (ARIMA foundation)	George Box and Gwilym Jenkins
Tips	Bayesian time series model	Time series forecasting model
Pirmavots	Pole, A., West, M., & Harrison, J. (1994). Applied Bayesian Forecasting and Time Series Analysis. Chapman & Hall. ISBN: 978-0412416903	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Citi nosaukumi	Bayesian ARIMA, BARIMA, Bayesian Box-Jenkins model, Bayesian integrated time series model	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Saistītās	6	6
Kopsavilkums	The Bayesian ARIMA model combines the classical Box-Jenkins ARIMA framework with Bayesian inference. Instead of obtaining single point estimates for autoregressive and moving average parameters, it places prior distributions over them and uses observed data to update beliefs into a full posterior distribution, enabling coherent uncertainty quantification and probabilistic forecasting.	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.