44 citations · 77 across the 13 of their papers we have counts for
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Deep Integro-Difference Equation Models for Spatio-Temporal Forecasting
Andrew Zammit-Mangion, Christopher K. Wikle
Integro-difference equation (IDE) models describe the conditional dependence between the spatial process at a future time point and the process at the present time point through an…
Comparison of Deep Neural Networks and Deep Hierarchical Models for Spatio-Temporal Data
Christopher K. Wikle
Spatio-temporal data are ubiquitous in the agricultural, ecological, and environmental sciences, and their study is important for understanding and predicting a wide variety of pro…
Deep Echo State Networks with Uncertainty Quantification for Spatio-Temporal Forecasting
Patrick L. McDermott, Christopher K. Wikle
Long-lead forecasting for spatio-temporal systems can often entail complex nonlinear dynamics that are difficult to specify it a priori. Current statistical methodologies for model…
An Ensemble Quadratic Echo State Network for Nonlinear Spatio-Temporal Forecasting
Patrick L. McDermott, Christopher K. Wikle
Spatio-temporal data and processes are prevalent across a wide variety of scientific disciplines. These processes are often characterized by nonlinear time dynamics that include in…