Linganisha mbinu

	Uchanganuzi wa Mfululizo wa Wakati Uliokatizwa (ITS)	Mfululizo wa Wakati wa Muundo wa Kibayesiani
Nyanja	Uhitimisho wa Kisababishi	Mbinu za Bayes
Familia	Regression model	Bayesian methods
Mwaka wa asili	2002	2014
Mwanzilishi	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)	Scott & Varian (2014); Brodersen et al. (2015)
Aina	Quasi-experimental segmented regression	State-space model / Bayesian structural model
Chanzo asilia	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.	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.
Majina mbadala	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi	BSTS, Bayesian Yapısal Zaman Serisi (BSTS), bayesian state-space model, causal impact model
Zinazohusiana	5	5
Muhtasari	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.	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.