Single exponential smoothing method and neural network in one method for time series prediction
Date Issued
2004-12-01
Author(s)
Risteski, Dimce
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 neurat 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.
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 neurat 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.
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