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	ऑटोरेग्रेसिव इंटीग्रेटेड मूविंग एवरेज (ARIMA) मॉडल	ग्रेडिएंट बूस्टिंग	क्वांटाइल रिग्रेशन
क्षेत्र	अर्थमिति	मशीन अधिगम	अर्थमिति
परिवार	Regression model	Machine learning	Regression model
उद्भव वर्ष	2015	2001	1978
प्रवर्तक	Box & Jenkins (Box-Jenkins methodology)	Friedman, J. H.	Koenker & Bassett
प्रकार	Univariate time-series model	Ensemble (sequential boosting of decision trees)	Conditional quantile regression
मौलिक स्रोत	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
उपनाम	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	conditional quantile regression, regression quantiles, Kantil Regresyon
संबंधित	5	5	5
सारांश	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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.