Model based method for estimating an attractor dimension from uni/multivariate chaotic time series with application to Bremen climatic dynamics
DOI10.1016/S0960-0779(03)00300-XzbMath1085.37504OpenAlexW2034441650MaRDI QIDQ1877955
Publication date: 19 August 2004
Published in: Chaos, Solitons and Fractals (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0960-0779(03)00300-x
predictionmodelingtime seriesLyapunov exponentssimulation resultstestingclimate datapolynomial autoregressive modelattractor embedding dimensionBremen citychaotic benchmark systems
Applications of dynamical systems (37N99) Strange attractors, chaotic dynamics of systems with hyperbolic behavior (37D45) Time series analysis of dynamical systems (37M10) Simulation of dynamical systems (37M05) Dimension theory of smooth dynamical systems (37C45)
Cites Work
- Unnamed Item
- Measuring the strangeness of strange attractors
- Extracting qualitative dynamics from experimental data
- Nonlinear prediction of chaotic time series
- Estimating the embedding dimension
- Embedology
- Practical method for determining the minimum embedding dimension of a scalar time series
- Adaptive control
- Fractal dimensional analysis of Indian climatic dynamics
- A method of embedding dimension estimation based on symplectic geometry
This page was built for publication: Model based method for estimating an attractor dimension from uni/multivariate chaotic time series with application to Bremen climatic dynamics
