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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...

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Detalles Bibliográficos
Autores principales: Guo, Zhiqiang, Wang, Huaiqing, Liu, Quan, Yang, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
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.
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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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