Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель ARIMA (Авторегресійна інтегрована ковзна середня)× | Байєсівська регресія× | Метод Монте-Карло на основі ланцюгів Маркова (MCMC)× | |
|---|---|---|---|
| Галузь≠ | Економетрика | Баєсові методи | Баєсові методи |
| Родина≠ | Regression model | Bayesian methods | Bayesian methods |
| Рік появи≠ | 2015 | — | — |
| Автор методу≠ | Box & Jenkins (Box-Jenkins methodology) | — | — |
| Тип≠ | Univariate time-series model | Bayesian linear model | Posterior sampling algorithm |
| Основоположне джерело≠ | 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 | 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 |
| Інші назви | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | bayesian linear regression, probabilistic regression, bayesian regresyon | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Пов'язані≠ | 5 | 2 | 3 |
| Підсумок≠ | 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). | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. | 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. |
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