Open AccessPrediction of chaotic time series based on fractal self-affinity
Author(s) -
Tao He,
Zhou Zheng-ou
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.56.693
Subject(s) - chaotic , fractal , attractor , series (stratigraphy) , computer science , affine transformation , function series , lorenz system , trajectory , statistical physics , algorithm , mathematics , mathematical analysis , physics , artificial intelligence , geometry , paleontology , fourier series , astronomy , biology
Based on the fractal structure of strange attractor and self-affine property of time series, a method is proposed for predicting chaotic time series. The algorithm first exploits the iterative function system to track current chaotic trajectory and selects the segment which possesses the best self-affine property of the time series statistically. Then the prediction model is constructed according to attractor and coverage theorem. To illustrate the performance of the proposed model, simulations are performed on the chaotic Mackey-Glass time series, EEG signal and Lorenz chaotic system. The results show that the chaotic time series are accurately predicted, which demonstrates the effectiveness of the model.