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.

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This paper introduces Lags COevolving with Rbfns (L-Co-R), a coevolutionary method developed to face time-series forecasting problems. L-Co-R simultaneously evolves the model that provides the forecasted values and the set of time lags the model must use in the prediction process. Coevolution takes place by means of two populations that evolve at the same time, cooperating between them; the first population is composed of radial basis function neural networks; the second one contains the individuals representing the sets of lags. Thus, the final solution provided by the method comprises both the neural net and the set of lags that better approximate the time series. The method has been tested across 34 different time series datasets, and the results compared to 6 different methods referenced in literature, and with respect to 4 different error measures. The results show that L-Co-R outperforms the rest of methods, as the statistical analysis carried out indicates.

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