পদ্ধতির তুলনা করুন

	Seasonal ARIMA (SARIMA)	ইটিএস: ত্রুটি, প্রবণতা, মৌসুমী সূচকীয় মসৃণকরণ	প্রফেট
ক্ষেত্র	অর্থমিতি	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model	Regression model
উদ্ভবের বছর	2015	2008	2018
প্রবর্তক	Box & Jenkins (seasonal extension of ARIMA)	Hyndman, Koehler, Ord & Snyder (state space framework)	Taylor & Letham (Facebook/Meta)
ধরন	Seasonal time-series model	Exponential smoothing state space model	Decomposable (structural) time series model
মৌলিক উৎস	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	Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer.	Taylor, S. J. & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45.
অপর নাম	seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA	exponential smoothing state space model, innovations state space model, Holt-Winters family, ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme	Prophet, Facebook Prophet, Meta Prophet, forecasting at scale
সম্পর্কিত	5	5	5
সারসংক্ষেপ	SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period.	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.	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.