Σύγκριση μεθόδων

	Βαϋεσιανή Δομική Ανάλυση Χρονοσειρών	Μοντέλο ARIMA (Autoregressive Integrated Moving Average)	Ανάλυση Διακοπτόμενης Χρονοσειράς (ITS)
Πεδίο	Μπεϋζιανή Στατιστική	Οικονομετρία	Αιτιακή Συμπερασματολογία
Οικογένεια	Bayesian methods	Regression model	Regression model
Έτος προέλευσης	2014	2015	2002
Δημιουργός	Scott & Varian (2014); Brodersen et al. (2015)	Box & Jenkins (Box-Jenkins methodology)	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Τύπος	State-space model / Bayesian structural model	Univariate time-series model	Quasi-experimental segmented regression
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	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
Συναφείς	5	5	5
Σύνοψη	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.