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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Web Browser-Based Forecasting of Economic Time-Series
verfasst von : V. M. Rivas, E. Parras-Gutiérrez, J. J. Merelo, M. G. Arenas, P. García-Fernández
Erschienen in: Decision Economics, In Commemoration of the Birth Centennial of Herbert A. Simon 1916-2016 (Nobel Prize in Economics 1978)
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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.