Prediction of Time Series Using RBF Neural Networks: A New Approach of Clustering

In this paper, we deal with the problem of time series prediction from a given set of input/output data. This problemconsists of the prediction of future values based on past and/or present data. We present a new method for prediction of timeseries data using radial basis functions. This approach is based on a new efficient method of clustering of the centers of theradial basis function neural network; it uses the error committed in every cluster using the real output of the radial basisfunction neural network trying to concentrate more clusters in those input regions where the error is bigger and move theclusters instead of just the input values of the I/O data. This method of clustering, improves the performance of the time seriesprediction system obtained, compared with other methods derived from traditional algorithms