Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data

Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. Recently, as deep learning models have become more common, RNNs have been used to forec...

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Auteurs principaux:	Patrick L. McDermott, Christopher K. Wikle
Format:	Artigo
Langue:	Inglês
Publié:	MDPI AG 2019-02-01
Collection:	Entropy
Sujets:	recurrent neural network Bayesian machine learning nonlinear dynamical models long-lead forecasting spatial-temporal
Accès en ligne:	https://www.mdpi.com/1099-4300/21/2/184
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