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| 시계열 예측을 위한 Conformal Prediction× | 최소제곱법(OLS) 회귀× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2021 | 2019 |
| 창시자≠ | Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI) | Wooldridge (textbook treatment); classical least squares |
| 유형≠ | Distribution-free prediction interval wrapper | Linear regression |
| 원전≠ | Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 별칭 | conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 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). | 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). |
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