Single exponential smoothing method and neural network in one method for time series prediction

Abstract

The purpose of this paper is to present a new method that combines statistical techniques and neural networks in one method for the better time series prediction. In this paper we presented single exponential smoothing method (statistical technique) merged with feed forward back propagation neural network in one method named as smart single exponential smoothing method (SSESM). The basic idea of the new method is to learn from the mistakes. More specifically, our neural network learns from the mistakes made by the statistical techniques. The mistakes are made by the smoothing parameter, which is constant. In our method, the smoothing parameter is a variable. It is changed according to the prediction of the neural network. Experimental results show that the prediction with a variable smoothing parameter is better than with a constant smoothing parameter