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| Predizione conforme per previsioni di serie temporali× | Regressione quantilica× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2021 | 1978 |
| Ideatore≠ | Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI) | Koenker & Bassett |
| Tipo≠ | Distribution-free prediction interval wrapper | Conditional quantile regression |
| Fonte seminale≠ | Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias≠ | conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi) | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Correlati≠ | 4 | 5 |
| Sintesi≠ | 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). | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
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