Salīdzināt metodes

	ARIMA (autoregresīvais integrētais slīdošā vidējā) modelis	Generalizētā autoregresīvā nosacītā heteroskedastiskuma (GARCH) modelis	SARIMA (Seasonālais ARIMA)
Nozare	Ekonometrija	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	2015	1986	2015
Autors	Box & Jenkins (Box-Jenkins methodology)	Tim Bollerslev	Box & Jenkins (seasonal extension of ARIMA)
Tips	Univariate time-series model	Conditional volatility model	Seasonal time-series model
Pirmavots	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	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.	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
Citi nosaukumi	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli	seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA
Saistītās	5	5	5
Kopsavilkums	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).	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.	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.