Comparer des méthodes

	Modèle ARIMA (Autoregressive Integrated Moving Average)	Chaîne de Markov Monte Carlo (MCMC)
Domaine	Économétrie	Bayésien
Famille	Regression model	Bayesian methods
Année d'origine	2015	—
Auteur d'origine	Box & Jenkins (Box-Jenkins methodology)	—
Type	Univariate time-series model	Posterior sampling algorithm
Source fondatrice	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	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
Alias	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
Apparentées	5	3
Résumé	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.