手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| ARIMA（自己回帰和分移動平均）モデル× | ベイズ回帰× | |
|---|---|---|
| 分野≠ | 計量経済学 | ベイズ |
| 系統≠ | Regression model | Bayesian methods |
| 提唱年≠ | 2015 | — |
| 提唱者≠ | Box & Jenkins (Box-Jenkins methodology) | — |
| 種類≠ | Univariate time-series model | Bayesian linear model |
| 原典≠ | 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 |
| 別名 | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | bayesian linear regression, probabilistic regression, bayesian regresyon |
| 関連≠ | 5 | 2 |
| 概要≠ | 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. |
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