@inproceedings{496,
  author = {V. Rivas and Elisabet Parras Gutiérrez and J. Merelo and M. Arenas and P. Garcia-Fernandez},
  editor = {Edgardo Bucciarelli and Marcello Silvestri and Sara González},
  title = {Web Browser-Based Forecasting of Economic Time-Series},
  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.},
  year = {2016},
  pages = {35-42},
  publisher = {Springer International Publishing},
  address = {Cham},
  isbn = {978-3-319-40111-9},
}