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| Model ARIMA (Autoregressive Integrated Moving Average)× | DeepAR× | Slučajna šuma× | |
|---|---|---|---|
| Oblast≠ | Ekonometrija | Duboko učenje | Mašinsko učenje |
| Porodica≠ | Regression model | Machine learning | Machine learning |
| Godina nastanka≠ | 2015 | 2020 | 2001 |
| Tvorac≠ | Box & Jenkins (Box-Jenkins methodology) | Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon) | Breiman, L. |
| Tip≠ | Univariate time-series model | Autoregressive recurrent neural network (probabilistic forecasting) | Ensemble (bagging of decision trees) |
| Temeljni izvor≠ | 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 | Salinas, D., Flunkert, V., Gasthaus, J. & Januschowski, T. (2020). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. International Journal of Forecasting, 36(3), 1181–1191. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Drugi nazivi≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Srodne≠ | 5 | 5 | 4 |
| Sažetak≠ | 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). | DeepAR is Amazon's industrial forecasting model, introduced by Salinas, Flunkert and Gasthaus (2017; published 2020), that uses an autoregressive recurrent neural network to estimate the parameters of a probability distribution at each step, producing a confidence interval rather than a single point forecast. It can model many related time series jointly within one model. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
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