Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Temporal Fusion Transformer× | DeepAR× | |
|---|---|---|
| Fagområde | Dyb læring | Dyb læring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2021 | 2020 |
| Ophavsperson≠ | Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T. | Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon) |
| Type≠ | Attention-based deep learning forecasting architecture | Autoregressive recurrent neural network (probabilistic forecasting) |
| Oprindelig kilde≠ | Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T. (2021). Temporal Fusion Transformers for Interpretable Multi-Horizon Time Series Forecasting. International Journal of Forecasting, 37(4), 1748–1764. DOI ↗ | 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 ↗ |
| Aliasser | Temporal Fusion Transformer (TFT), TFT, interpretable multi-horizon forecasting transformer | DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR |
| Relaterede≠ | 6 | 5 |
| Resumé≠ | The Temporal Fusion Transformer (TFT), introduced by Lim, Arık, Loeff and Pfister in 2021, is an interpretable deep learning architecture for multi-horizon time series forecasting. It combines variable selection, gating, multi-horizon attention and quantile outputs, processing static, past and known-future inputs together to produce multi-step forecasts. | 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. |
| ScholarGateDatasæt ↗ |
|
|