So sánh phương pháp

	Mô hình ARIMA (Autoregressive Integrated Moving Average)	Hồi quy Bayes	Phân tích chuỗi thời gian bị gián đoạn (ITS)
Lĩnh vực	Kinh tế lượng	Bayes	Suy luận nhân quả
Họ	Regression model	Bayesian methods	Regression model
Năm ra đời	2015	—	2002
Người khởi xướng	Box & Jenkins (Box-Jenkins methodology)	—	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Loại	Univariate time-series model	Bayesian linear model	Quasi-experimental segmented regression
Công trình gốc	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.
Tên gọi khác	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
Liên quan	5	2	5
Tóm tắt	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.