Linganisha mbinu

	Mfumo wa Nafasi ya Hali (Kichujio cha Kalman)	Mfumo wa ARIMA (Autoregressive Integrated Moving Average)	Uchanganuzi wa Regresheni wa Kiotomatiki wa Bayesian (BVAR)	Muundo wa Wakati wa Muundo (Muundo wa Msingi wa Muundo)
Nyanja	Ekonometriki	Ekonometriki	Ekonometriki	Ekonometriki
Familia	Regression model	Regression model	Regression model	Regression model
Mwaka wa asili	1990	2015	1986	1990
Mwanzilishi	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Box & Jenkins (Box-Jenkins methodology)	Litterman (1986); Bańbura, Giannone & Reichlin (2010)	Andrew C. Harvey
Aina	State space time series model	Univariate time-series model	Bayesian multivariate time-series model	State-space (unobserved components) time series model
Chanzo asilia	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.	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	Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737
Majina mbadala	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)	BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)
Zinazohusiana	4	5	5	4
Muhtasari	A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases.	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 VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts.	The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit.