State-space reconstruction and prediction of chaotic time series based on fuzzy clustering

2005 ◽  
Author(s):  
J. Abonyi ◽  
B. Feil ◽  
S. Nemeth ◽  
P. Arva ◽  
R. Babuska
Keyword(s):  
Time Series ◽  
State Space ◽  
Fuzzy Clustering ◽  
Chaotic Time Series ◽  
State Space Reconstruction

Nonuniform State Space Reconstruction for Multivariate Chaotic Time Series

2019 ◽  
Vol 49 (5) ◽  
pp. 1885-1895 ◽  
Author(s):  
Min Han ◽  
Weijie Ren ◽  
Meiling Xu ◽  
Tie Qiu
Keyword(s):  
Time Series ◽  
State Space ◽  
Chaotic Time Series ◽  
State Space Reconstruction

State-space prediction model for chaotic time series

1998 ◽  
Vol 58 (2) ◽  
pp. 2640-2643 ◽  
Author(s):  
A. K. Alparslan ◽  
M. Sayar ◽  
A. R. Atilgan
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
State Space ◽  
Chaotic Time Series

State Space Reconstruction of Nonstationary Time-Series

2016 ◽  
Vol 12 (3) ◽  
Author(s):  
Hong-Guang Ma ◽  
Chun-Liang Zhang ◽  
Fu Li
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
State Space ◽  
Phase Angle ◽  
Analytical Form ◽  
Nonstationary Time Series ◽  
Original Time Series ◽  
State Space Reconstruction ◽  
Original Time ◽  
The Hilbert Transform

In this paper, a new method of state space reconstruction is proposed for the nonstationary time-series. The nonstationary time-series is first converted into its analytical form via the Hilbert transform, which retains both the nonstationarity and the nonlinear dynamics of the original time-series. The instantaneous phase angle θ is then extracted from the time-series. The first- and second-order derivatives θ˙, θ¨ of phase angle θ are calculated. It is mathematically proved that the vector field [θ,θ˙,θ¨] is the state space of the original time-series. The proposed method does not rely on the stationarity of the time-series, and it is available for both the stationary and nonstationary time-series. The simulation tests have been conducted on the stationary and nonstationary chaotic time-series, and a powerful tool, i.e., the scale-dependent Lyapunov exponent (SDLE), is introduced for the identification of nonstationarity and chaotic motion embedded in the time-series. The effectiveness of the proposed method is validated.

scholarly journals Prediction of chaotic time series based on hierarchical fuzzy-clustering

2006 ◽  
Vol 55 (7) ◽  
pp. 3302
Author(s):  
Liu Fu-Cai ◽  
Sun Li-Ping ◽  
Liang Xiao-Ming
Keyword(s):  
Time Series ◽  
Fuzzy Clustering ◽  
Chaotic Time Series

scholarly journals On the Investigation of State Space Reconstruction of Nonlinear Aeroelastic Response Time Series

2006 ◽  
Vol 13 (4-5) ◽  
pp. 393-407 ◽  
Author(s):  
Flávio D. Marques ◽  
Eduardo M. Belo ◽  
Vilma A. Oliveira ◽  
José R. Rosolen ◽  
Andréia R. Simoni
Keyword(s):  
Experimental Data ◽  
Time Series ◽  
Response Time ◽  
State Space ◽  
The State ◽  
Aeroelastic Response ◽  
State Space Reconstruction ◽  
Wing Model ◽  
Linear Behavior ◽  
Non Linear

Stall-induced aeroelastic motion may present severe non-linear behavior. Mathematical models for predicting such phenomena are still not available for practical applications and they are not enough reliable to capture physical effects. Experimental data can provide suitable information to help the understanding of typical non-linear aeroelastic phenomena. Dynamic systems techniques based on time series analysis can be adequately applied to non-linear aeroelasticity. When experimental data are available, the methods of state space reconstruction have been widely considered. This paper presents the state space reconstruction approach for the characterization of the stall-induced aeroelastic non-linear behavior. A wind tunnel scaled wing model has been tested. The wing model is subjected to different airspeeds and dynamic incidence angle variations. The method of delays is used to identify an embedded attractor in the state space from experimentally acquired aeroelastic response time series. To obtain an estimate of the time delay used in the state space reconstruction from time series, the autocorrelation function analyis is used. For the calculation of the embedding dimension the correlation integral approach is considered. The reconstructed attractors can reveal typical non-linear structures associated, for instance, to chaos or limit cycles.

scholarly journals Use of False Nearest Neighbours for Selecting Variables and Embedding Parameters for State Space Reconstruction

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Anna Krakovská ◽  
Kristína Mezeiová ◽  
Hana Budáčová
Keyword(s):  
Time Series ◽  
State Space ◽  
Time Window ◽  
Nearest Neighbour ◽  
Embedding Dimension ◽  
State Space Reconstruction ◽  
Dimensional Phase Space ◽  
Nearest Neighbours ◽  
Takens Theorem ◽  
The One

If data are generated by a system with a d-dimensional attractor, then Takens’ theorem guarantees that reconstruction that is diffeomorphic to the original attractor can be built from the single time series in 2d+1-dimensional phase space. However, under certain conditions, reconstruction is possible even in a space of smaller dimension. This topic is very important because the size of the reconstruction space relates to the effectiveness of the whole subsequent analysis. In this paper, the false nearest neighbour (FNN) methods are revisited to estimate the optimum embedding parameters and the most appropriate observables for state space reconstruction. A modification of the false nearest neighbour method is introduced. The findings contribute to evidence that the length of the embedding time window (TW) is more important than the reconstruction delay time and the embedding dimension (ED) separately. Moreover, if several time series of the same system are observed, the choice of the one that is used for the reconstruction could also be critical. The results are demonstrated on two chaotic benchmark systems.

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