Σύγκριση μεθόδων

	Conformal Prediction για Πρόβλεψη Χρονοσειρών	Παλινδρόμηση Ελαχίστων Τετραγώνων (OLS)
Πεδίο	Οικονομετρία	Οικονομετρία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	2021	2019
Δημιουργός	Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI)	Wooldridge (textbook treatment); classical least squares
Τύπος	Distribution-free prediction interval wrapper	Linear regression
Θεμελιώδης πηγή	Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Εναλλακτικές ονομασίες	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
Συναφείς	4	5
Σύνοψη	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).