Chaotic Time Series Prediction Using Immune Optimization Theory

DOI

10.2991/ijcis.2010.3.s1.4How to use a DOI?

Keywords

Chaotic time series, phase space reconstruction, immune optimization theory, prediction.

Abstract

To solve chaotic time series prediction problem, a novel Prediction approach for chaotic time series based on Immune Optimization Theory (PIOT) is proposed. In PIOT, the concepts and formal definitions of antigen, antibody and affinity being used for time series prediction are given, and the mathematical models of immune optimization operators being used for establishing time series prediction model are exhibited. Chaotic time series is analyzed and corresponding sample space is reconstructed by phase space reconstruction method; then, the prediction model of chaotic time series is constructed by immune optimization theory; finally, using this prediction model to forecast chaotic time series. To demonstrate the effectiveness of PIOT, the three typical chaotic nonlinear time series are generated by nonlinear dynamics systems that are Lorenz, Mackey-Glass and Henon, respectively, and are used for simulating prediction. The simulation results show that PIOT is a feasible and effective prediction method, and meanwhile provides a novel prediction approach for chaotic time series.

Copyright

© 2010, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Yuanquan Shi
AU  - Xiaojie Liu
AU  - Tao Li
AU  - Xiaoning Peng
AU  - Wen Chen
AU  - Ruirui Zhang
AU  - Yanming Fu
PY  - 2010
DA  - 2010/12/01
TI  - Chaotic Time Series Prediction Using Immune Optimization Theory
JO  - International Journal of Computational Intelligence Systems
SP  - 43
EP  - 60
VL  - 3
IS  - Supplement 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2010.3.s1.4
DO  - 10.2991/ijcis.2010.3.s1.4
ID  - Shi2010
ER  -