ORDER SELECTION OF AUTOREGRESSIVE MODEL
Order selection is an important step in autoregressive (AR) modeling. It may be executed through examining the stationary process and the behavior of residuals. In this thesis, the New Final Prediction Error (NFPE) approach was applied by calculating the expectation of prediction error. The best...
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id-itb.:339252019-01-31T10:09:58ZORDER SELECTION OF AUTOREGRESSIVE MODEL Hasanah Akmecia, Miftahul Matematika Indonesia Theses stationarity, autoregressive, order selection, prediction error, minimum variance INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/33925 Order selection is an important step in autoregressive (AR) modeling. It may be executed through examining the stationary process and the behavior of residuals. In this thesis, the New Final Prediction Error (NFPE) approach was applied by calculating the expectation of prediction error. The best order was selected based on the criteria of minimum prediction error values. As illustrations, several simulations for dierent cases, which consider the combination of parameter values and the order of AR model, were performed. For each case of these simulations, the Akaike Information Criterion method (AIC), Bayesian Information Criterion (BIC), and NFPE were compared for selecting the best order. It is obtained that NFPE give the best predictive results than the other two methods. text |
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Indonesia Indonesia |
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Matematika |
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Matematika Hasanah Akmecia, Miftahul ORDER SELECTION OF AUTOREGRESSIVE MODEL |
| description |
Order selection is an important step in autoregressive (AR) modeling. It may be
executed through examining the stationary process and the behavior of residuals.
In this thesis, the New Final Prediction Error (NFPE) approach was applied by
calculating the expectation of prediction error. The best order was selected based on
the criteria of minimum prediction error values. As illustrations, several simulations
for dierent cases, which consider the combination of parameter values and the
order of AR model, were performed. For each case of these simulations, the Akaike
Information Criterion method (AIC), Bayesian Information Criterion (BIC), and
NFPE were compared for selecting the best order. It is obtained that NFPE give
the best predictive results than the other two methods. |
| format |
Theses |
| author |
Hasanah Akmecia, Miftahul |
| author_facet |
Hasanah Akmecia, Miftahul |
| author_sort |
Hasanah Akmecia, Miftahul |
| title |
ORDER SELECTION OF AUTOREGRESSIVE MODEL |
| title_short |
ORDER SELECTION OF AUTOREGRESSIVE MODEL |
| title_full |
ORDER SELECTION OF AUTOREGRESSIVE MODEL |
| title_fullStr |
ORDER SELECTION OF AUTOREGRESSIVE MODEL |
| title_full_unstemmed |
ORDER SELECTION OF AUTOREGRESSIVE MODEL |
| title_sort |
order selection of autoregressive model |
| url |
https://digilib.itb.ac.id/gdl/view/33925 |
| _version_ |
1822924123574108160 |