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。
| ベイジアン構造時系列モデル× | ARIMA（自己回帰和分移動平均）モデル× | |
|---|---|---|
| 分野≠ | ベイズ | 計量経済学 |
| 系統≠ | Bayesian methods | Regression model |
| 提唱年≠ | 2014 | 2015 |
| 提唱者≠ | Scott & Varian (2014); Brodersen et al. (2015) | Box & Jenkins (Box-Jenkins methodology) |
| 種類≠ | State-space model / Bayesian structural model | Univariate time-series model |
| 原典≠ | Scott, S. L. & Varian, H. R. (2014). Predicting the Present with Bayesian Structural Time Series. International Journal of Mathematical Modelling and Numerical Optimisation, 5(1/2), 4–23. DOI ↗ | 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 |
| 別名≠ | BSTS, Bayesian Yapısal Zaman Serisi (BSTS), bayesian state-space model, causal impact model | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| 関連 | 5 | 5 |
| 概要≠ | Bayesian Structural Time Series (BSTS) is a state-space modelling framework, introduced by Scott and Varian (2014), that decomposes a time series into additive components — trend, seasonality, and regression — and estimates them jointly through Bayesian inference. It underpins Google's CausalImpact library and is a powerful tool for both forecasting and counterfactual causal analysis of interventions. | 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). |
| ScholarGateデータセット ↗ |
|
|