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

Area of Science:

• Machine Learning
• Dynamical Systems
• Computational Science

Background:

• Recurrent neural networks (RNNs) are widely used for modeling complex sequential data.
• Forecasting intricate systems, particularly dynamical spatio-temporal processes, presents significant challenges.
• Existing RNN models often lack rigorous uncertainty quantification, hindering reliable predictions.

Purpose of the Study:

• To develop a Bayesian recurrent neural network (RNN) model for nonlinear spatio-temporal forecasting.
• To formally quantify uncertainty within RNNs while preserving forecast accuracy.
• To adapt RNN architectures for the specific characteristics of nonlinear spatio-temporal data.

Main Methods:

• Implementation of a fully Bayesian framework for RNNs.
• Introduction of modifications to basic RNNs to better handle spatio-temporal data.
• Application of the proposed Bayesian RNN to a Lorenz simulation and two real-world datasets.

Main Results:

• The Bayesian RNN model successfully quantified uncertainty in nonlinear spatio-temporal forecasting.
• The model demonstrated competitive forecast accuracy compared to standard RNNs.
• Adaptations improved the model's suitability for complex spatio-temporal dynamics.

Conclusions:

• Bayesian RNNs offer a robust framework for uncertainty quantification in complex forecasting tasks.
• The proposed model advances the application of deep learning to dynamical spatio-temporal systems.
• This approach provides more reliable predictions for intricate real-world phenomena.