Usporedite metode

	Model ARIMA (Autoregresivni integrirani pokretni prosjek)	Povećanje gradijenta	Regresija običnih najmanjih kvadrata (OLS)
Područje	Ekonometrija	Strojno učenje	Ekonometrija
Obitelj	Regression model	Machine learning	Regression model
Godina nastanka	2015	2001	2019
Tvorac	Box & Jenkins (Box-Jenkins methodology)	Friedman, J. H.	Wooldridge (textbook treatment); classical least squares
Vrsta	Univariate time-series model	Ensemble (sequential boosting of decision trees)	Linear regression
Temeljni izvor	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	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Drugi nazivi	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Srodne	5	5	5
Sažetak	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).	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	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).