Salīdzināt metodes

	Konformālā prognozēšana laika sēriju prognozēšanai	Gradient Boosting
Nozare	Ekonometrija	Mašīnmācīšanās
Saime	Regression model	Machine learning
Izcelsmes gads	2021	2001
Autors	Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI)	Friedman, J. H.
Tips	Distribution-free prediction interval wrapper	Ensemble (sequential boosting of decision trees)
Pirmavots	Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Citi nosaukumi	conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi)	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Saistītās	4	5
Kopsavilkums	Conformal prediction is a distribution-free wrapper that turns any point forecaster — ARIMA, a neural network, or a machine-learning model — into valid prediction intervals using only its residuals. The time-series form was popularised by Xu & Xie (2021) and the modern tutorial treatment by Angelopoulos & Bates (2023).	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.