Bayesian methods
贝叶斯结构时间序列
贝叶斯结构时间序列（Bayesian Structural Time Series, BSTS）是由Scott和Varian（2014）提出的一个状态空间建模框架，它将时间序列分解为加性分量——趋势、季节性和回归量——并通过贝叶斯推断联合估计它们。它支撑着Google的CausalImpact库，是用于干预预测和反事实因果分析的强大工具。
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来源
- 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: 10.1504/IJMMNO.2014.059942 ↗
- Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N. & Scott, S. L. (2015). Inferring Causal Impact Using Bayesian Structural Time-Series Models. Annals of Applied Statistics, 9(1), 247–274. DOI: 10.1214/14-AOAS788 ↗
如何引用本页
ScholarGate. (2026, June 1). Bayesian Structural Time Series Model. ScholarGate. https://scholargate.app/zh/bayesian/bayesian-structural-time-series
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- ARIMA（自回归积分滑动平均）模型计量经济学↔ compare
- Bayesian Regression贝叶斯↔ compare
- 中断时间序列（ITS）分析因果推断↔ compare
- 马尔可夫链蒙特卡洛 (MCMC)贝叶斯↔ compare
- 状态空间模型（卡尔曼滤波器）计量经济学↔ compare