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| Modelo ARIMA (Autoregressive Integrated Moving Average)× | Cadenas de Markov Monte Carlo (MCMC)× | |
|---|---|---|
| Campo≠ | Econometría | Bayesiano |
| Familia≠ | Regression model | Bayesian methods |
| Año de origen≠ | 2015 | — |
| Autor original≠ | Box & Jenkins (Box-Jenkins methodology) | — |
| Tipo≠ | Univariate time-series model | Posterior sampling algorithm |
| Fuente seminal≠ | 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) |
| Relacionados≠ | 5 | 3 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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