TY - JOUR
T1 - Model based method for estimating an attractor dimension from uni/multivariate chaotic time series with application to Bremen climatic dynamics
AU - Ataei, M.
AU - Lohmann, B.
AU - Khaki-Sedigh, A.
AU - Lucas, C.
N1 - Funding Information:
We would like to thank the DAAD (German Academic Exchange Service) for providing the financial assistance to make this research possible.
PY - 2004/3
Y1 - 2004/3
N2 - In this paper, a method for estimating an attractor embedding dimension based on polynomial models and its application in investigating the dimension of Bremen climatic dynamics are presented. The attractor embedding dimension provides the primary knowledge for analyzing the invariant characteristics of the attractor and determines the number of necessary variables to model the dynamics. Therefore, the optimality of this dimension has an important role in computational efforts, analysis of the Lyapunov exponents, and efficiency of modeling and prediction. The smoothness property of the reconstructed map implies that, there is no self-intersection in the reconstructed attractor. The method of this paper relies on testing this property by locally fitting a general polynomial autoregressive model to the given data and evaluating the normalized one step ahead prediction error. The corresponding algorithms are developed in uni/multivariate form and some probable advantages of using information from other time series are discussed. The effectiveness of the proposed method is shown by simulation results of its application to some well-known chaotic benchmark systems. Finally, the proposed methodology is applied to two major dynamic components of the climate data of the Bremen city to estimate the related minimum attractor embedding dimension.
AB - In this paper, a method for estimating an attractor embedding dimension based on polynomial models and its application in investigating the dimension of Bremen climatic dynamics are presented. The attractor embedding dimension provides the primary knowledge for analyzing the invariant characteristics of the attractor and determines the number of necessary variables to model the dynamics. Therefore, the optimality of this dimension has an important role in computational efforts, analysis of the Lyapunov exponents, and efficiency of modeling and prediction. The smoothness property of the reconstructed map implies that, there is no self-intersection in the reconstructed attractor. The method of this paper relies on testing this property by locally fitting a general polynomial autoregressive model to the given data and evaluating the normalized one step ahead prediction error. The corresponding algorithms are developed in uni/multivariate form and some probable advantages of using information from other time series are discussed. The effectiveness of the proposed method is shown by simulation results of its application to some well-known chaotic benchmark systems. Finally, the proposed methodology is applied to two major dynamic components of the climate data of the Bremen city to estimate the related minimum attractor embedding dimension.
UR - http://www.scopus.com/inward/record.url?scp=0141727699&partnerID=8YFLogxK
U2 - 10.1016/S0960-0779(03)00300-X
DO - 10.1016/S0960-0779(03)00300-X
M3 - Article
AN - SCOPUS:0141727699
SN - 0960-0779
VL - 19
SP - 1131
EP - 1139
JO - Chaos, Solitons and Fractals
JF - Chaos, Solitons and Fractals
IS - 5
ER -