Salīdzināt metodes

	ARIMA (autoregresīvais integrētais slīdošā vidējā) modelis	Vienkāršā un dubultā eksponenciālā izlīdzināšana (SES / Holt)	Generalizētā autoregresīvā nosacītā heteroskedastiskuma (GARCH) modelis	Parastā mazāko kvadrātu (OLS) regresija
Nozare	Ekonometrija	Ekonometrija	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model	Regression model	Regression model
Izcelsmes gads	2015	1957	1986	2019
Autors	Box & Jenkins (Box-Jenkins methodology)	Robert G. Brown (SES); Charles C. Holt (linear trend)	Tim Bollerslev	Wooldridge (textbook treatment); classical least squares
Tips	Univariate time-series model	Exponential smoothing forecasting model	Conditional volatility model	Linear regression
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	Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Citi nosaukumi	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt)	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Saistītās	5	3	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).	Exponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smoothing (SES), introduced by Robert G. Brown in 1959, forecasts series with a stable level, while Holt's double exponential smoothing, introduced by Charles C. Holt in 1957, adds a trend term using the parameters alpha and beta.	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.	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).