Machine learning
DeepAR
DeepAR 是亚马逊的工业级预测模型，由 Salinas、Flunkert 和 Gasthaus 于 2017 年（2020 年发表）提出，它使用自回归循环神经网络在每一步估计概率分布的参数，从而产生置信区间而非单一的点预测。它可以对许多相关的时间序列进行联合建模。
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来源
- 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: 10.1016/j.ijforecast.2019.07.001 ↗
- Salinas, D., Flunkert, V. & Gasthaus, J. (2017). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. arXiv:1704.04110. link ↗
如何引用本页
ScholarGate. (2026, June 1). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. ScholarGate. https://scholargate.app/zh/deep-learning/deepar
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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