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A Feature Fusion Based Forecasting Model for Financial Time Series
Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model i...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4074191/ https://www.ncbi.nlm.nih.gov/pubmed/24971455 http://dx.doi.org/10.1371/journal.pone.0101113 |
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author | Guo, Zhiqiang Wang, Huaiqing Liu, Quan Yang, Jie |
author_facet | Guo, Zhiqiang Wang, Huaiqing Liu, Quan Yang, Jie |
author_sort | Guo, Zhiqiang |
collection | PubMed |
description | Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. |
format | Online Article Text |
id | pubmed-4074191 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-40741912014-07-02 A Feature Fusion Based Forecasting Model for Financial Time Series Guo, Zhiqiang Wang, Huaiqing Liu, Quan Yang, Jie PLoS One Research Article Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. Public Library of Science 2014-06-27 /pmc/articles/PMC4074191/ /pubmed/24971455 http://dx.doi.org/10.1371/journal.pone.0101113 Text en © 2014 Guo et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Guo, Zhiqiang Wang, Huaiqing Liu, Quan Yang, Jie A Feature Fusion Based Forecasting Model for Financial Time Series |
title | A Feature Fusion Based Forecasting Model for Financial Time Series |
title_full | A Feature Fusion Based Forecasting Model for Financial Time Series |
title_fullStr | A Feature Fusion Based Forecasting Model for Financial Time Series |
title_full_unstemmed | A Feature Fusion Based Forecasting Model for Financial Time Series |
title_short | A Feature Fusion Based Forecasting Model for Financial Time Series |
title_sort | feature fusion based forecasting model for financial time series |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4074191/ https://www.ncbi.nlm.nih.gov/pubmed/24971455 http://dx.doi.org/10.1371/journal.pone.0101113 |
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