Feature selection for neural network based stock prediction
We propose a new methodology of feature selection for stock movement prediction. The methodology is based upon finding those features which minimize the correlation relation function. We first produce all the combination of feature and evaluate each of them by using our evaluate function. We search...
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th-cmuir.6653943832-431492017-09-28T06:51:06Z Feature selection for neural network based stock prediction Sugunnasil P. Somhom S. We propose a new methodology of feature selection for stock movement prediction. The methodology is based upon finding those features which minimize the correlation relation function. We first produce all the combination of feature and evaluate each of them by using our evaluate function. We search through the generated set with hill climbing approach. The self-organizing map based stock prediction model is utilized as the prediction method. We conduct the experiment on data sets of the Microsoft Corporation, General Electric Co. and Ford Motor Co. The results show that our feature selection method can improve the efficiency of the neural network based stock prediction. © 2010 Springer-Verlag. 2017-09-28T06:51:06Z 2017-09-28T06:51:06Z 2010-12-20 Book Series 18650929 2-s2.0-78650150569 10.1007/978-3-642-16699-0_15 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=78650150569&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/43149 |
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We propose a new methodology of feature selection for stock movement prediction. The methodology is based upon finding those features which minimize the correlation relation function. We first produce all the combination of feature and evaluate each of them by using our evaluate function. We search through the generated set with hill climbing approach. The self-organizing map based stock prediction model is utilized as the prediction method. We conduct the experiment on data sets of the Microsoft Corporation, General Electric Co. and Ford Motor Co. The results show that our feature selection method can improve the efficiency of the neural network based stock prediction. © 2010 Springer-Verlag. |
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Book Series |
author |
Sugunnasil P. Somhom S. |
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Sugunnasil P. Somhom S. Feature selection for neural network based stock prediction |
author_facet |
Sugunnasil P. Somhom S. |
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Sugunnasil P. |
title |
Feature selection for neural network based stock prediction |
title_short |
Feature selection for neural network based stock prediction |
title_full |
Feature selection for neural network based stock prediction |
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Feature selection for neural network based stock prediction |
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Feature selection for neural network based stock prediction |
title_sort |
feature selection for neural network based stock prediction |
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2017 |
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=78650150569&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/43149 |
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