@inproceedings{8fc335d94c6542d3a706f5d59486ee1d,
title = "Forecasting stock index trend based on the GAS-VM integrated system and wavelet-based feature extractions on multiple scales",
abstract = "This paper proposes a novel GA-SVM integrated system for stock trend prediction based on wavelet-based feature extractions on multiple scales. The parameters of support vector machine (SVM) and kernel function are optimized by Genetic Algorithm (GA). Wavelet transformation is used to form the wavelet-scaling features. The Shanghai Stock Exchange (SSE) Composite index is selected for this study. Sufficient experiments are carried out, resulting in significant performances of the novel GA-SVM integrated system based on the wavelet-based feature selection method.",
keywords = "genetic algorithm, integrated system, prediction, support vector machines, wavelet analysis",
author = "Chen, \{Sheng Li\} and Li, \{Yi Jun\} and Qiang Ye",
year = "2011",
doi = "10.1109/ICEMMS.2011.6015721",
language = "英语",
isbn = "9781424496631",
series = "ICEMMS 2011 - Proceedings: 2011 2nd IEEE International Conference on Emergency Management and Management Sciences",
pages = "468--472",
booktitle = "ICEMMS 2011 - Proceedings",
note = "2011 2nd IEEE International Conference on Emergency Management and Management Sciences, ICEMMS 2011 ; Conference date: 08-08-2011 Through 10-08-2011",
}