Ensemble Classification over Stock Market Time Series and Economy News

Seker S. E. , Mert C., Al-Naami K., Ayan U., Özalp N.

11th IEEE International Conference on Intelligence and Security Informatics (IEEE ISI), Washington, United States Of America, 4 - 07 June 2013, pp.272-273 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/isi.2013.6578840
  • City: Washington
  • Country: United States Of America
  • Page Numbers: pp.272-273


Aim of this study is applying the ensemble classification methods over the stock market closing values, which can be assumed as time series and finding out the relation between the economy news. In order to keep the study back ground clear, the majority voting method has been applied over the three classification algorithms, which are the k-nearest neighborhood, support vector machine and the C4.5 tree. The results gathered from two different feature extraction methods are correlated with majority voting meta classifier (ensemble method) which is running over three classifiers. The results show the success rates are increased after the ensemble at least 2 to 3 percent success rate.