مقایسهٔ روش‌ها

	سری‌های زمانی ساختاری بیزی	مدل آریما (میانگین متحرک یکپارچه خودرگرسیو)	رگرسیون بیزی	تحلیل سری زمانی مقطع‌دار (ITS)
حوزه	بیزی	اقتصادسنجی	بیزی	استنتاج علّی
خانواده	Bayesian methods	Regression model	Bayesian methods	Regression model
سال پیدایش	2014	2015	—	2002
پدیدآور	Scott & Varian (2014); Brodersen et al. (2015)	Box & Jenkins (Box-Jenkins methodology)	—	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
نوع	State-space model / Bayesian structural model	Univariate time-series model	Bayesian linear model	Quasi-experimental segmented regression
منبع بنیادین	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	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	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.
نام‌های دیگر	BSTS, Bayesian Yapısal Zaman Serisi (BSTS), bayesian state-space model, causal impact model	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	bayesian linear regression, probabilistic regression, bayesian regresyon	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
مرتبط	5	5	2	5
خلاصه	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).	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.	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.