Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| N-BEATS× | Model ARIMA (autoregresní integrovaný klouzavý průměr)× | DeepAR× | |
|---|---|---|---|
| Obor≠ | Hluboké učení | Ekonometrie | Hluboké učení |
| Rodina≠ | Machine learning | Regression model | Machine learning |
| Rok vzniku≠ | 2020 | 2015 | 2020 |
| Tvůrce≠ | Oreshkin, B.N. et al. | Box & Jenkins (Box-Jenkins methodology) | Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon) |
| Typ≠ | Deep neural forecasting architecture (interpretable basis expansion) | Univariate time-series model | Autoregressive recurrent neural network (probabilistic forecasting) |
| Původní zdroj≠ | Oreshkin, B.N. et al. (2020). N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting. ICLR. link ↗ | 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 ↗ |
| Další názvy | N-BEATS — Nöral Zaman Serisi Tahmini, Neural Basis Expansion Analysis, neural basis expansion | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR |
| Příbuzné | 5 | 5 | 5 |
| Shrnutí≠ | N-BEATS is a deep learning architecture for time series forecasting, introduced by Oreshkin and colleagues in 2020, built from interpretable trend and seasonality stacks. It was the first purely neural forecasting model to reach state-of-the-art performance on the M4 competition without relying on any classical statistical components. | 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. |
| ScholarGateDatová sada ↗ |
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