Predicting spatio-temporal time series using dimension reduced local states

DOI10.1007/s00332-019-09588-7zbMath1441.37089arXiv1904.06089OpenAlexW2982254562MaRDI QIDQ2179862

Ulrich Parlitz, George Datseris, Jonas Isensee

Publication date: 13 May 2020

Published in: Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1904.06089

zbMATH Keywords

spatio-temporal chaosdata driven modellingnearest neighbours prediction

Mathematics Subject Classification ID

Time series analysis of dynamical systems (37M10)

Related Items (1)

Convolutional autoencoder and conditional random fields hybrid for predicting spatial-temporal chaos

Uses Software

• Julia
• GitHub
• DifferentialEquations.jl
• NearestNeighbors.jl
• NeuralPDE.jl

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Extracting qualitative dynamics from experimental data
• Embedology
• Regularized local linear prediction of chaotic time series
• Minimal model for human ventricular action potentials in tissue
• Julia: A Fresh Approach to Numerical Computing
• WINNING ENTRY OF THE K. U. LEUVEN TIME-SERIES PREDICTION COMPETITION
• On Flame Propagation Under Conditions of Stoichiometry
• Multidimensional binary search trees used for associative searching
• Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks
• Nonlinear time-series analysis revisited
• NONPARAMETRIC STATISTICAL MODELING OF SPATIOTEMPORAL DYNAMICS BASED ON RECORDED DATA
• Nonlinear Time Series Analysis
• An Introduction to Statistical Learning
• State-Space Reconstruction and Spatio-Temporal Prediction of Lattice Dynamical Systems
• Identification of coupled map lattice models of complex spatio-temporal patterns

This page was built for publication: Predicting spatio-temporal time series using dimension reduced local states