Comparer des méthodes

	TBATS	SARIMA (Seasonal ARIMA)	Décomposition STL : Décomposition Saisonnier-Tendance par Loess
Domaine	Économétrie	Économétrie	Économétrie
Famille	Regression model	Regression model	Process / pipeline
Année d'origine	2011	2015	1990
Auteur d'origine	De Livera, Hyndman & Snyder	Box & Jenkins (seasonal extension of ARIMA)	Cleveland, Cleveland, McRae & Terpenning
Type	Exponential smoothing state space model	Seasonal time-series model	nonparametric iterative smoother
Source fondatrice	De Livera, A. M., Hyndman, R. J. & Snyder, R. D. (2011). Forecasting Time Series with Complex Seasonal Patterns Using Exponential Smoothing. Journal of the American Statistical Association, 106(496), 1513-1527.	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	Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 6(1), 3–73.
Alias	trigonometric exponential smoothing, multiple seasonal exponential smoothing, complex seasonal exponential smoothing, TBATS — Çoklu Mevsimsel Üstel Düzleştirme	seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA	Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL)
Apparentées	3	5	3
Résumé	TBATS is an innovations state space forecasting model, introduced by De Livera, Hyndman and Snyder (2011), that combines a Box-Cox transformation, ARMA errors and trigonometric (Fourier) seasonal terms. It is built to handle continuous time series with several nested seasonal cycles at once — for example hourly data that also repeats daily, weekly and yearly.	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.	STL Decomposition, introduced by Cleveland, Cleveland, McRae, and Terpenning (1990), is a nonparametric procedure that separates a time series into three additive components — trend, seasonal, and remainder — using iterative locally weighted regression (loess). Widely used in economics, meteorology, and data science, it handles time series of any periodicity and is robust to the presence of outliers, making it a highly flexible alternative to classical decomposition methods.