@article {doi:10.1002/isaf.1409,
	title = {Time series forecasting using evolutionary neural nets implemented in a volunteer computing system},
	journal = {Intelligent Systems in Accounting, Finance and Management},
	volume = {24},
	number = {2-3},
	year = {2017},
	pages = {87-95},
	abstract = {Summary 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.},
	keywords = {evolutionary computation, fintech, radial basis function neural networks, time-series forecasting, volunteer computation, Web-based programming},
	doi = {10.1002/isaf.1409},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/isaf.1409},
	author = {Rivas, V.M. and E. Parras-Guti{\'e}rrez and Merelo, J.J. and Arenas, M.G. and Garc{\'\i}a-Fern{\'a}ndez, P.}
}
