Сравнение на методи

	Тройно експоненциално изглаждане по Холт-Уинтърс	Модел в състояние пространство (Калманов филтър)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1960	1990
Създател	Charles C. Holt and Peter R. Winters	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Тип	Exponential smoothing forecasting model	State space time series model
Основополагащ източник	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.
Други названия	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)
Свързани	4	4
Резюме	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.