Deep echo state networks with uncertainty quantification for spatio-temporal forecasting
From MaRDI portal
Publication:6626063
DOI10.1002/env.2553zbMATH Open1545.62867MaRDI QIDQ6626063
Christopher K. Wikle, Patrick L. McDermott
Publication date: 28 October 2024
Published in: Environmetrics (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Reservoir computing approaches to recurrent neural network training
- A general science-based framework for dynamical spatio-temporal models
- Validity of the parametric bootstrap for goodness-of-fit testing in semiparametric models
- Inference from iterative simulation using multiple sequences
- Analog forecasting with dynamics-adapted kernels
- Deterministic Nonperiodic Flow
- Statistical Agent-Based Models for Discrete Spatio-Temporal Systems
- Introduction to Genetic Algorithms
- Neural networks and physical systems with emergent collective computational abilities.
- The elements of statistical learning. Data mining, inference, and prediction
- Physically motivated scale interaction parameterization in reduced rank quadratic nonlinear dynamic spatio‐temporal models
- A model‐based approach for analog spatio‐temporal dynamic forecasting
- An ensemble quadratic echo state network for non-linear spatio-temporal forecasting
- Modern perspectives on statistics for spatio-temporal data
- Sparsity in nonlinear dynamic spatiotemporal models using implied advection
Related Items (7)
Data-driven stochastic model for cross-interacting processes with different time scales ⋮ A high-resolution bilevel skew-\(t\) stochastic generator for assessing Saudi Arabia's wind energy resources ⋮ Ensemble forecasting of the Zika space-time spread with topological data analysis ⋮ An illustration of model agnostic explainability methods applied to environmental data ⋮ Reds: random ensemble deep spatial prediction ⋮ Calibrated forecasts of quasi-periodic climate processes with deep echo state networks and penalized quantile regression ⋮ Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks
This page was built for publication: Deep echo state networks with uncertainty quantification for spatio-temporal forecasting