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| 시계열 예측을 위한 Conformal Prediction× | 그래디언트 부스팅× | |
|---|---|---|
| 분야≠ | 계량경제학 | 머신러닝 |
| 계열≠ | Regression model | Machine learning |
| 기원 연도≠ | 2021 | 2001 |
| 창시자≠ | Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI) | Friedman, J. H. |
| 유형≠ | Distribution-free prediction interval wrapper | Ensemble (sequential boosting of decision trees) |
| 원전≠ | Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| 별칭 | conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi) | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| 관련≠ | 4 | 5 |
| 요약≠ | 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. |
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