পদ্ধতির তুলনা করুন

	ইটিএস: ত্রুটি, প্রবণতা, মৌসুমী সূচকীয় মসৃণকরণ	ARIMA (Autoregressive Integrated Moving Average) মডেল	সরল ও দ্বৈত সূচকীয় মসৃণীকরণ (SES / Holt)
ক্ষেত্র	অর্থমিতি	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model	Regression model
উদ্ভবের বছর	2008	2015	1957
প্রবর্তক	Hyndman, Koehler, Ord & Snyder (state space framework)	Box & Jenkins (Box-Jenkins methodology)	Robert G. Brown (SES); Charles C. Holt (linear trend)
ধরন	Exponential smoothing state space model	Univariate time-series model	Exponential smoothing forecasting model
মৌলিক উৎস	Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer.	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	Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill.
অপর নাম	exponential smoothing state space model, innovations state space model, Holt-Winters family, ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt)
সম্পর্কিত	5	5	3
সারসংক্ষেপ	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.	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).	Exponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smoothing (SES), introduced by Robert G. Brown in 1959, forecasts series with a stable level, while Holt's double exponential smoothing, introduced by Charles C. Holt in 1957, adds a trend term using the parameters alpha and beta.