A method of selecting similar learning data in the prediction of time series using neural networks

This paper explores a method of improving the predictive performance by the multilayer feedforward neural network in time series prediction. For the similar data selective learning method, we propose a method of weighting the distance by a power function of correlation coefficients for the time series (CSDS method). The results of numerical experiments show that with the case of a time series whose nature is rather choppy or chaotic, using the CSDS method appropriately is considerably effective to improve the prediction performance and its performance is considerably better than that by other previously proposed methods