activity
20092026
most citedDeep Integro-Difference Equation Models for Spatio-Temporal Forecasting

44 citations · 80 across the 25 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

stat.ME2023

A Criterion for Aggregation Error for Multivariate Spatial Data

Ranadeep Daw, Jonathan R. Bradley, Christopher K. Wikle +1

The criterion for aggregation error (CAGE) is an important metric that aims to measure errors that arise in multiscale (or multi-resolution) spatial data, referred to as the modifi…

physics.ao-ph2023

Calibrated Forecasts of Quasi-Periodic Climate Processes with Deep Echo State Networks and Penalized Quantile Regression

Matthew Bonas, Christopher K. Wikle, Stefano Castruccio

Among the most relevant processes in the Earth system for human habitability are quasi-periodic, ocean-driven multi-year events whose dynamics are currently incompletely characteri…

stat.ML2023

Flexible and efficient emulation of spatial extremes processes via variational autoencoders

Likun Zhang, Xiaoyu Ma, Christopher K. Wikle +1

Many real-world processes have complex tail dependence structures that cannot be characterized using classical Gaussian processes. More flexible spatial extremes models exhibit app…

stat.AP20231 cited

Bayesian Ensemble Echo State Networks for Enhancing Binary Stochastic Cellular Automata

Nicholas Grieshop, Christopher K. Wikle

Binary spatio-temporal data are common in many application areas. Such data can be considered from many perspectives, including via deterministic or stochastic cellular automata, w…

stat.AP2023

Data-Driven Modeling of Wildfire Spread with Stochastic Cellular Automata and Latent Spatio-Temporal Dynamics

Nicholas Grieshop, Christopher K. Wikle

We propose a Bayesian stochastic cellular automata modeling approach to model the spread of wildfires with uncertainty quantification. The model considers a dynamic neighborhood st…