Chaotic Analysis and Dimension Reduction of Shanghai Composite Index Time Series

DOI

10.2991/icmmita-16.2016.136How to use a DOI?

Keywords

Dimension reduction; principal component analysis; chaos theory; Shanghai Composite Index

Abstract

Correctly understanding the fluctuation of the securities market has a great effect on Chinese economic development. In order to verify the chaotic characteristics of Shanghai Composite Index time series, we use G-P algorithm and Cao method to calculate the characteristic quantities such as embedding dimension, correlation dimension and Lyapunov exponent. If the embedding dimension of the time series is large, it will be more complicated to analyze its behavior characteristics. Therefore, we further apply principal component analysis, by using less comprehensive indicators exist in the variables to analyze various information in order to achieve the purpose of dimension reduction.

Copyright

© 2017, 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  - CONF
AU  - Peiqi Zhen
AU  - Yue Zeng
AU  - Jianding Zhou
AU  - Lijun Lv
PY  - 2017/01
DA  - 2017/01
TI  - Chaotic Analysis and Dimension Reduction of Shanghai Composite Index Time Series
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 736
EP  - 739
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.136
DO  - 10.2991/icmmita-16.2016.136
ID  - Zhen2017/01
ER  -