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| Séries temporelles structurelles bayésiennes× | Modèle ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Domaine≠ | Bayésien | Économétrie |
| Famille≠ | Bayesian methods | Regression model |
| Année d'origine≠ | 2014 | 2015 |
| Auteur d'origine≠ | Scott & Varian (2014); Brodersen et al. (2015) | Box & Jenkins (Box-Jenkins methodology) |
| Type≠ | State-space model / Bayesian structural model | Univariate time-series model |
| Source fondatrice≠ | 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 |
| Alias≠ | BSTS, Bayesian Yapısal Zaman Serisi (BSTS), bayesian state-space model, causal impact model | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Apparentées | 5 | 5 |
| Résumé≠ | 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). |
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