পদ্ধতির তুলনা করুন

	প্রফেট	ইটিএস: ত্রুটি, প্রবণতা, মৌসুমী সূচকীয় মসৃণকরণ	সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন	স্টেট স্পেস মডেল (কালম্যান ফিল্টার)
ক্ষেত্র	অর্থমিতি	অর্থমিতি	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model	Regression model	Regression model
উদ্ভবের বছর	2018	2008	2019	1990
প্রবর্তক	Taylor & Letham (Facebook/Meta)	Hyndman, Koehler, Ord & Snyder (state space framework)	Wooldridge (textbook treatment); classical least squares	Harvey; Durbin & Koopman (state space treatment); Kalman filter
ধরন	Decomposable (structural) time series model	Exponential smoothing state space model	Linear regression	State space time series model
মৌলিক উৎস	Taylor, S. J. & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45.	Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
অপর নাম	Prophet, Facebook Prophet, Meta Prophet, forecasting at scale	exponential smoothing state space model, innovations state space model, Holt-Winters family, ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
সম্পর্কিত	5	5	5	4
সারসংক্ষেপ	Prophet is a Bayesian structural time series model introduced by Taylor and Letham at Facebook/Meta in 2018. It forecasts a continuous series by decomposing it into separate, interpretable trend, seasonality, and holiday components, and is designed to be approachable for analysts working at scale.	ETS is a comprehensive exponential smoothing framework that automatically selects additive or multiplicative combinations of the error (E), trend (T) and seasonal (S) components of a time series. Formalised as an innovations state space model by Hyndman, Koehler, Ord and Snyder in 2008, it unifies and generalises the Holt-Winters family of forecasting methods.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.