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Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
Patrick L. McDermott, Christopher K. Wikle
Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential…
A Hierarchical Spatio-Temporal Analog Forecasting Model for Count Data
Patrick L. McDermott, Christopher K. Wikle, Joshua Millspaugh
1. Analog forecasting has been successful at producing robust forecasts for a variety of ecological and physical processes. Analog forecasting is a mechanism-free nonlinear method…
A Model-Based Approach for Analog Spatio-Temporal Dynamic Forecasting
Patrick L. McDermott, Christopher K. Wikle
Analog forecasting has been applied in a variety of fields for predicting future states of complex nonlinear systems that require flexible forecasting methods. Past analog methods…