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	स्टेट स्पेस मॉडल (कलमन फिल्टर)	संरचनात्मक समय श्रृंखला मॉडल (मूल संरचनात्मक मॉडल)
क्षेत्र	अर्थमिति	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1990	1990
प्रवर्तक	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Andrew C. Harvey
प्रकार	State space time series model	State-space (unobserved components) time series model
मौलिक स्रोत	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737
उपनाम	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)	BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)
संबंधित	4	4
सारांश	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.	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.