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Web Browser-Based Forecasting of Economic Time-Series

  • V. M. Rivas
  • E. Parras-Gutiérrez
  • J. J. Merelo
  • M. G. Arenas
  • P. García-Fernández
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 475)

Abstract

This paper presents the implementation of a time series forecasting algorithm, jsEvRBF, that uses genetic algorithm and neural nets in a way that can be run in must modern web browsers. Using browsers to run forecasting algorithms is a challenge, since language support and performance varies across implementations of the JavaScript virtual machine and vendor. However, their use will provide a boost in the number of platforms available for scientists. jsEvRBF is written in JavaScript, so that it can be easily delivered to and executed by any device containing a web-browser just accessing an URL. The experiments show the results yielded by the algorithm over a data set related to currencies exchange. Best results achieved can be effectively compared against previous results in literature, though robustness of the new algorithm has to be improved.

Keywords

Time-series forecasting Evolutionary computation Radial Basis Function Neural Networks Web-based programming Volunteer computation 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • V. M. Rivas
    • 1
    • 3
  • E. Parras-Gutiérrez
    • 1
  • J. J. Merelo
    • 2
    • 3
  • M. G. Arenas
    • 2
    • 3
  • P. García-Fernández
    • 2
    • 3
  1. 1.Department of Computer SciencesUniversity of JaenJaénSpain
  2. 2.Depto. de Arquitectura Y Tecnologías de las Computadoras, Depto. de Electrónica y Tecnologías de las Computadoras, Department of Computers, Architecture and TechnologyUniv. de Granada, SPAIN University of GranadaGranadaSpain
  3. 3.GeNeura TeamGranadaSpain

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