方法对比

	Conformal Prediction for Time-Series Forecasting	梯度提升（Gradient Boosting）	普通最小二乘法 (OLS) 回归
领域	计量经济学	机器学习	计量经济学
方法族	Regression model	Machine learning	Regression model
起源年份	2021	2001	2019
提出者	Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI)	Friedman, J. H.	Wooldridge (textbook treatment); classical least squares
类型	Distribution-free prediction interval wrapper	Ensemble (sequential boosting of decision trees)	Linear regression
开创性文献	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
别名	conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi)	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
相关	4	5	5
摘要	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.	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).