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.6653943832-506902018-09-04T04:44:24Z Feature selection for neural network based stock prediction Prompong Sugunnasil Samerkae Somhom Computer Science 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. 2018-09-04T04:44:24Z 2018-09-04T04:44:24Z 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/6653943832/50690 |
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Chiang Mai University Library |
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Thailand |
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Computer Science |
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Computer Science Prompong Sugunnasil Samerkae Somhom Feature selection for neural network based stock prediction |
| description |
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 |
Prompong Sugunnasil Samerkae Somhom |
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Prompong Sugunnasil Samerkae Somhom |
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Prompong Sugunnasil |
| 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 |
| title_fullStr |
Feature selection for neural network based stock prediction |
| title_full_unstemmed |
Feature selection for neural network based stock prediction |
| title_sort |
feature selection for neural network based stock prediction |
| publishDate |
2018 |
| url |
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=78650150569&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/50690 |
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