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

Mohammed Awad2, Héctor Pomares1, Ignacio Rojas1, Osama Salameh2 and Mai Hamdon2
1Department of Computer Architecture and Technology, University of Granada, Spain
2Faculty of information Technology, Arab American University, Palestine

Abstract: In this paper, we 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. We 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. This method of clustering, improves the performance of the time series prediction system obtained, compared with other methods derived from traditional algorithms.