Chaotic Dynamics Analysis and Forecast of Stock Time Series

Author

Liu, Hongjie ; Huang, Dongwei ; Wang, Yongzhao

Author_Institution

Sch. of Sci., Tianjin Polytech. Univ., Tianjin, China

fYear

2011

fDate

16-17 July 2011

Firstpage

75

Lastpage

78

Abstract

In this paper, the time series which formed by the daily closing price of the Shanghai stock composite index and the daily opening price of Huaxia Bank have been studied. The log-linear detrending (LLD) method is used to treat the data, then based on phase space reconstruction, it has been proved that the studied time series have the chaotic behavior by drawing phase diagram, calculating the characteristic parameters of time series like correlation dimension and the largest Lyapunov exponent. Finally, the Back Propagation (BP) neural network is adopted to forecast the further data of time series, and the satisfying forecast result is obtained.

Keywords

backpropagation; banking; forecasting theory; neural nets; nonlinear control systems; stock markets; time series; Huaxia Bank; Lyapunov exponent; Shanghai stock composite index; backpropagation; chaotic dynamics analysis; daily closing price; daily opening price; drawing phase diagram; log-linear detrending method; neural network; phase space reconstruction; stock time series forecasting; Chaos; Correlation; Delay effects; Nonlinear dynamical systems; Stock markets; Time series analysis; Trajectory; BP neural network; chaos; correlation dimension; phase diagram; the largest Lyapunov exponent;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Society (ISCCS), 2011 International Symposium on

Conference_Location

Kota Kinabalu

Print_ISBN

978-1-4577-0644-8

Type

conf

DOI

10.1109/ISCCS.2011.28

Filename

6004269