Comparison of the bootstrap and bootstrap neural network methods in non linear time series

Document Type : Original Paper

Authors

1 Department of Statistics, University of Isfahan

2 Department of Information Technology Engineering , University of Isfahan

Abstract

Neural networks are among those mathematical models which are used to model non-linear time series with high accuracy. The advantage with these linear times series as opposed to topical ones is that they don’t require restrictive assumptions. The accuracy of neural network based estimators as nonparametric models is of high importance. In that light, we can use bootstrapping to calculate the accuracy of estimators in the time series’ complex nonlinear structures. Though introduced in recent years these methods yield more accurate results in the bias calculation of estimators compared to the other ones. This paper introduces neural network bootstrap, bootstrap autoregressive, moving block bootstrap method and residual bootstrap methods in time series. Then these four algorithms are compared with each other in a simulation study. Finally an example related to Iran’s kerosene price monthly data is worked out.

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References

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History

  • Receive Date: 23 July 2014
  • Revise Date: 03 January 2015
  • Accept Date: 06 March 2015

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