مقایسهٔ روش‌ها

	مدل آریما (میانگین متحرک یکپارچه خودرگرسیو)	هموارسازی نمایی ساده و دوگانه (SES / Holt)	هموارسازی نمایی سه‌گانه هولت-وینترز	مدل فضای حالت (فیلتر کالمن)
حوزه	اقتصادسنجی	اقتصادسنجی	اقتصادسنجی	اقتصادسنجی
خانواده	Regression model	Regression model	Regression model	Regression model
سال پیدایش	2015	1957	1960	1990
پدیدآور	Box & Jenkins (Box-Jenkins methodology)	Robert G. Brown (SES); Charles C. Holt (linear trend)	Charles C. Holt and Peter R. Winters	Harvey; Durbin & Koopman (state space treatment); Kalman filter
نوع	Univariate time-series model	Exponential smoothing forecasting model	Exponential smoothing forecasting model	State space time series model
منبع بنیادین	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.	Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324-342.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
نام‌های دیگر	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)	triple exponential smoothing, Winters' method, Holt-Winters seasonal method, Holt-Winters Üçlü Üstel Düzleştirme	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
مرتبط	5	3	4	4
خلاصه	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.	Holt-Winters triple exponential smoothing is a forecasting model that extends Holt's double smoothing by adding a seasonal component, introduced by Peter Winters in 1960 building on Charles Holt's work. It tracks three evolving quantities — level, trend, and season — and combines them to forecast a continuous time series.	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.