Vertaile menetelmiä
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| ARIMA (Autoregressive Integrated Moving Average) -malli× | Gradient Boosting× | |
|---|---|---|
| Tieteenala≠ | Ekonometria | Koneoppiminen |
| Menetelmäperhe≠ | Regression model | Machine learning |
| Syntyvuosi≠ | 2015 | 2001 |
| Kehittäjä≠ | Box & Jenkins (Box-Jenkins methodology) | Friedman, J. H. |
| Tyyppi≠ | Univariate time-series model | Ensemble (sequential boosting of decision trees) |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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. |
| ScholarGateAineisto ↗ |
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