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	Bayesian Structural Time Series	Analiza przerwanych szeregów czasowych (ITS)
Dziedzina	Statystyka bayesowska	Wnioskowanie przyczynowe
Rodzina	Bayesian methods	Regression model
Rok powstania	2014	2002
Twórca	Scott & Varian (2014); Brodersen et al. (2015)	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Typ	State-space model / Bayesian structural model	Quasi-experimental segmented regression
Źródło pierwotne	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.	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.
Inne nazwy	BSTS, Bayesian Yapısal Zaman Serisi (BSTS), bayesian state-space model, causal impact model	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Pokrewne	5	5
Podsumowanie	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.	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.