Linganisha mbinu

	Utabiri Konformali kwa Utabiri wa Mfululizo wa Wakati	Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)
Nyanja	Ekonometriki	Ekonometriki
Familia	Regression model	Regression model
Mwaka wa asili	2021	2019
Mwanzilishi	Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI)	Wooldridge (textbook treatment); classical least squares
Aina	Distribution-free prediction interval wrapper	Linear regression
Chanzo asilia	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
Majina mbadala	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
Zinazohusiana	4	5
Muhtasari	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).