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| Модел ARIMA (Autoregressive Integrated Moving Average)× | Градиентен бустинг× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|---|
| Област≠ | Иконометрия | Машинно обучение | Иконометрия |
| Семейство≠ | Regression model | Machine learning | Regression model |
| Година на възникване≠ | 2015 | 2001 | 2019 |
| Създател≠ | Box & Jenkins (Box-Jenkins methodology) | Friedman, J. H. | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Univariate time-series model | Ensemble (sequential boosting of decision trees) | Linear regression |
| Основополагащ източник≠ | 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 ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Други названия≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Свързани | 5 | 5 | 5 |
| Резюме≠ | 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. | 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). |
| ScholarGateНабор от данни ↗ |
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