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Improving Financial Time Series Prediction Through Output Classification by a Neural Network Ensemble

  • Felipe GiacomelEmail author
  • Adriano C. M. Pereira
  • Renata Galante
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9262)

Abstract

One topic of great interest in the literature is time series prediction. This kind of prediction, however, does not have to provide the exact future values every time: in some cases, knowing only time series future tendency is enough. In this paper, we propose a neural network ensemble that receives as input the last values from a time series and returns not its future values, but a prediction that indicates whether the next value will raise or fall down. We perform exhaustive experiments to analyze our method by using time series extracted from the North American stock market, and evaluate the hit rate and amount of profit that could be obtained by performing the operations recommended by the system. Evaluation results show capital increases up to 56 %.

Keywords

Artificial neural networks Classification Prediction Stock markets Time series 

Notes

Acknowledgments

This work is partially supported by InWeb (Brazilian National Institute for Web Research), under the MCT/CNPq grant 45.7488/2014-0 and by the authors individual grants and scholarships from CNPq (National Counsel of Technological and Scientific Development) and CAPES (Coordination for the Improvement of Higher Education Personnel).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Felipe Giacomel
    • 1
    Email author
  • Adriano C. M. Pereira
    • 2
  • Renata Galante
    • 1
  1. 1.Department of Computer ScienceFederal University of Rio Grande do Sul (UFRGS)Porto AlegreBrazil
  2. 2.Department of Computer ScienceFederal University of Minas Gerais (UFMG)Belo HorizonteBrazil

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