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 webbrowser 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.
CITATION STYLE
Rivas, V. M., Parras-Gutiérrez, E., Merelo, J. J., Arenas, M. G., & García-Fernández, P. (2016). Web browser-based forecasting of economic time-series. In Advances in Intelligent Systems and Computing (Vol. 475, pp. 35–42). Springer Verlag. https://doi.org/10.1007/978-3-319-40111-9_5
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