TY - JOUR
ID - 11046
TI - Comparison of the bootstrap and bootstrap neural network methods in non linear time series
JO - Journal of Advanced Mathematical Modeling
JA - JAMM
LA - en
SN - 2251-8088
AU - Isapareh, Masoud
AU - Iranpanah, Nasrollah
AU - Kaedi, Marjan
AD - Department of Statistics, University of Isfahan
AD - Department of Information Technology Engineering , University of Isfahan
Y1 - 2014
PY - 2014
VL - 4
IS - 1
SP - 85
EP - 106
KW - Artificial neural network
KW - Auto regregressive bootstrap
KW - Residual bootstrap
KW - Moving block bootstrap
KW - Nonlinear time series
KW - Monte Carlo
DO -
N2 - 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.
UR - https://jamm.scu.ac.ir/article_11046.html
L1 - https://jamm.scu.ac.ir/article_11046_8cee2eb66efa288bef544c98a6ba8ae1.pdf
ER -