So sánh phương pháp

	Chuỗi thời gian cấu trúc Bayes	Mô hình ARIMA (Autoregressive Integrated Moving Average)	Phân tích chuỗi thời gian bị gián đoạn (ITS)	Chuỗi Markov Monte Carlo (MCMC)
Lĩnh vực	Bayes	Kinh tế lượng	Suy luận nhân quả	Bayes
Họ	Bayesian methods	Regression model	Regression model	Bayesian methods
Năm ra đời	2014	2015	2002	—
Người khởi xướng	Scott & Varian (2014); Brodersen et al. (2015)	Box & Jenkins (Box-Jenkins methodology)	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)	—
Loại	State-space model / Bayesian structural model	Univariate time-series model	Quasi-experimental segmented regression	Posterior sampling algorithm
Công trình gốc	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.	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	Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.	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
Tên gọi khác	BSTS, Bayesian Yapısal Zaman Serisi (BSTS), bayesian state-space model, causal impact model	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
Liên quan	5	5	5	3
Tóm tắt	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).	Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope.	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.