Open AccessA new method to recognize determinism in time series
Author(s) -
YanDong Wu,
Hao Xie
Publication year - 2007
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.56.6294
Subject(s) - series (stratigraphy) , symplectic geometry , nonlinear system , measure (data warehouse) , lorenz system , surrogate data , determinism , computer science , noise (video) , singular spectrum analysis , singular value decomposition , time series , spectrum (functional analysis) , mathematics , algorithm , chaotic , mathematical analysis , image (mathematics) , artificial intelligence , physics , statistics , data mining , paleontology , quantum mechanics , biology
Compared with singular value decomposition, symplectic geometry spectrum is a measure preserving and nonlinear transform. So, it is more suitable for nonlinear dynamics system analysis. A new method to detect determinism in time series based on symplectic geometry spectrum (SGS) is proposed in the present work. Chaos and stochastic process could be recognized by applying the non_parameter Mann_Whitney on the SGS of original data and its surrogate data. The method is first tested on stochastic processes, the Lorenz, Rossler, Mackey_Glass and high dimensional coupling equations. Then it is applied to two data sets of Santa Fe to test its effect on experimental data. Finally, the robust ness of the method is tested on the time series with different data length and different levels of additive noise.