Сравнение методов

	Конформное прогнозирование для временных рядов	Случайный лес
Область	Эконометрика	Машинное обучение
Семейство	Regression model	Machine learning
Год появления	2021	2001
Автор метода	Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI)	Breiman, L.
Тип	Distribution-free prediction interval wrapper	Ensemble (bagging of decision trees)
Основополагающий источник	Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Другие названия	conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi)	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Связанные	4	4
Сводка	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).	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.