পদ্ধতির তুলনা করুন

	স্টেট স্পেস মডেল (কালম্যান ফিল্টার)	ARIMA (Autoregressive Integrated Moving Average) মডেল	মার্কভ রিজিমি-সুইচিং মডেল (MS-AR / MS-VAR)	স্ট্রাকচারাল টাইম সিরিজ মডেল (বেসিক স্ট্রাকচারাল মডেল)
ক্ষেত্র	অর্থমিতি	অর্থমিতি	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model	Regression model	Regression model
উদ্ভবের বছর	1990	2015	1989	1990
প্রবর্তক	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Box & Jenkins (Box-Jenkins methodology)	Hamilton (1989); Kim & Nelson (1999)	Andrew C. Harvey
ধরন	State space time series model	Univariate time-series model	Regime-switching time series model	State-space (unobserved components) time series model
মৌলিক উৎস	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	Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737
অপর নাম	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR	BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)
সম্পর্কিত	4	5	5	4
সারসংক্ষেপ	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).	The Markov regime-switching model lets the parameters of a time series change probabilistically across hidden regimes governed by a Markov chain. Introduced by Hamilton (1989) and developed further by Kim and Nelson (1999), it automatically detects business-cycle phases such as expansions and contractions.	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.