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.
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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: Bucciarelli, E., Silvestri, M., Rodríguez González, S. (eds) Decision Economics, In Commemoration of the Birth Centennial of Herbert A. Simon 1916-2016 (Nobel Prize in Economics 1978). Advances in Intelligent Systems and Computing, vol 475. Springer, Cham. https://doi.org/10.1007/978-3-319-40111-9_5
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DOI: https://doi.org/10.1007/978-3-319-40111-9_5
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