Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| DeepAR× | Informer× | |
|---|---|---|
| Vakgebied | Deep learning | Deep learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2020 | 2021 |
| Grondlegger≠ | Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon) | Zhou, H. et al. |
| Type≠ | Autoregressive recurrent neural network (probabilistic forecasting) | Transformer (ProbSparse self-attention) |
| Oorspronkelijke bron≠ | 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 ↗ | Zhou, H. et al. (2021). Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. AAAI. DOI ↗ |
| Aliassen | DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR | Informer — Uzun Dizi Transformer Tahmini, Informer transformer, ProbSparse attention forecaster |
| Verwant | 5 | 5 |
| Samenvatting≠ | 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. | Informer is a Transformer-based model introduced by Zhou et al. in 2021 for long-sequence time-series forecasting, using a ProbSparse self-attention mechanism that lowers the computational complexity of the standard Transformer to O(L log L). It is built for problems that demand predictions across thousands of future steps. |
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