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	संरचनात्मक समय श्रृंखला मॉडल (मूल संरचनात्मक मॉडल)	बाइयेसियन स्ट्रक्चरल टाइम सीरीज़
क्षेत्र	अर्थमिति	बायेसियन
परिवार	Regression model	Bayesian methods
उद्भव वर्ष	1990	2014
प्रवर्तक	Andrew C. Harvey	Scott & Varian (2014); Brodersen et al. (2015)
प्रकार	State-space (unobserved components) time series model	State-space model / Bayesian structural model
मौलिक स्रोत	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737	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.
उपनाम	BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)	BSTS, Bayesian Yapısal Zaman Serisi (BSTS), bayesian state-space model, causal impact model
संबंधित	4	5
सारांश	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.	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.