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| Конформно прогнозиране за прогнозиране на времеви редове× | Модел ARIMA (Autoregressive Integrated Moving Average)× | Градиентен бустинг× | |
|---|---|---|---|
| Област≠ | Иконометрия | Иконометрия | Машинно обучение |
| Семейство≠ | Regression model | Regression model | Machine learning |
| Година на възникване≠ | 2021 | 2015 | 2001 |
| Създател≠ | Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI) | Box & Jenkins (Box-Jenkins methodology) | Friedman, J. H. |
| Тип≠ | Distribution-free prediction interval wrapper | Univariate time-series model | Ensemble (sequential boosting of decision trees) |
| Основополагащ източник≠ | Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591. DOI ↗ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Други названия≠ | conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Свързани≠ | 4 | 5 | 5 |
| Резюме≠ | 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). | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | 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. |
| ScholarGateНабор от данни ↗ |
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