Сравнение методов

	Обобщенная авторегрессионная условная гетероскедастичность (GARCH)	TBATS
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1986	2011
Автор метода	Tim Bollerslev	De Livera, Hyndman & Snyder
Тип	Conditional volatility model	Exponential smoothing state space model
Основополагающий источник	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.	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.
Другие названия	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli	trigonometric exponential smoothing, multiple seasonal exponential smoothing, complex seasonal exponential smoothing, TBATS — Çoklu Mevsimsel Üstel Düzleştirme
Связанные	5	3
Сводка	GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks and past variances, capturing the volatility clustering seen in returns.	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.