Prediction of Time Series Using RBF Neural Networks: A New Approach of Clustering
Source: University of Granada
In this paper, the authors deal with the problem of time series prediction from a given set of input/output data. This problem consists of the prediction of future values based on past and/or present data. They present a new method for prediction of time series data using radial basis functions. This approach is based on a new efficient method of clustering of the centers of the radial basis function neural network; it uses the error committed in every cluster using the real output of the radial basis function neural network trying to concentrate more clusters in those input regions where the error is bigger and move the clusters instead of just the input values of the I/O data.