Comparar métodos

	Prophet	Suavizado exponencial triple de Holt-Winters	Regresión por Mínimos Cuadrados Ordinarios (MCO)	Modelo de espacio de estados (Filtro de Kalman)
Campo	Econometría	Econometría	Econometría	Econometría
Familia	Regression model	Regression model	Regression model	Regression model
Año de origen	2018	1960	2019	1990
Autor original	Taylor & Letham (Facebook/Meta)	Charles C. Holt and Peter R. Winters	Wooldridge (textbook treatment); classical least squares	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Tipo	Decomposable (structural) time series model	Exponential smoothing forecasting model	Linear regression	State space time series model
Fuente seminal	Taylor, S. J. & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45.	Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324-342.	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.
Alias	Prophet, Facebook Prophet, Meta Prophet, forecasting at scale	triple exponential smoothing, Winters' method, Holt-Winters seasonal method, Holt-Winters Üçlü 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)
Relacionados	5	4	5	4
Resumen	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.	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.	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.