Chaotic Analysis and Dimension Reduction of Shanghai Composite Index Time Series
Authors
Peiqi Zhen, Yue Zeng, Jianding Zhou, Lijun Lv
Corresponding Author
Peiqi Zhen
Available Online January 2017.
- 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/).
Cite this article
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 -