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Time series forecasting using evolutionary neural nets implemented in a volunteer computing system

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Listed:
  • V.M. Rivas
  • E. Parras‐Gutiérrez
  • J.J. Merelo
  • M.G. Arenas
  • P. García‐Fernández

Abstract

jsEvRBF is a time‐series forecasting method based on genetic algorithm and neural nets. Written in JavaScript language, can be executed in most web browsers. Consequently, everybody can participate in the experiments, and scientists can take advantage of nowadays available browsers and devices as computation environments. This is also a great challenge as the language support and performance varies from one browser to another. In this paper, jsEvRBF has been tested in a volunteer computing experiment, and also in a single‐browser one. Both experiments are related to forecasting currencies exchange, and the results show the viability of the proposal.

Suggested Citation

  • V.M. Rivas & E. Parras‐Gutiérrez & J.J. Merelo & M.G. Arenas & P. García‐Fernández, 2017. "Time series forecasting using evolutionary neural nets implemented in a volunteer computing system," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 24(2-3), pages 87-95, April.
  • Handle: RePEc:wly:isacfm:v:24:y:2017:i:2-3:p:87-95
    DOI: 10.1002/isaf.1409
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    References listed on IDEAS

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    1. Junseok Hwang & Jorn Altmann & Ashraf Bany Mohammed, 2010. "Determinants of Participation in Global Volunteer Grids: A Cross-Country Analysis," TEMEP Discussion Papers 201040, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Jan 2010.
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    1. Ashraf Bany Mohammed & Jorn Altmann, 2010. "A Funding and Governing Model for Achieving Sustainable Growth of Computing e-Infrastructures," TEMEP Discussion Papers 201062, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Jun 2010.

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